{ "count" : 86151, "data" : [ { "id" : "6a2ca5bd74d8ee00a09b7ee8", "title" : "Principal Data Scientist", "company" : "Intellishore", "industry" : "Consulting", "salary" : { "min" : 950000.0, "max" : 1450000.0, "currency" : "DKK", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Denmark, adjusted for executive seniority, biotech industry, and Copenhagen city tier." }, "location" : "Copenhagen", "locations" : [ "Copenhagen" ], "timestamp" : "2026-06-16T00:46:31.765", "fingerprint" : "9488786ac1ba448028ae7190dbf3c956e5ec1dfccd326cf39dd5db85c2c2d3e4", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/intellishoredk/jobs/7724568003", "externalId" : "7724568003", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Copenhagen", "country" : "Denmark", "continent" : "eu", "source" : "llm", "workMode" : "hybrid", "geo" : "55.6867243,12.5700724" } ], "locationText" : "Copenhagen, Denmark", "employmentType" : "FULL_TIME", "experienceMinYears" : 8, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Machine learning, Forecasting", "professionalSkills" : [ "Client engagements", "Business critical programs", "Commercial measurement", "Data science workstream", "Methodology development", "Offering development" ], "hardSkillsList" : [ "Machine learning", "Forecasting", "Causal inference", "Measurement" ], "hardSkills" : [ { "name" : "Machine learning", "level" : "intermediate" }, { "name" : "Forecasting", "level" : "intermediate" }, { "name" : "Causal inference", "level" : "intermediate" }, { "name" : "Measurement", "level" : "intermediate" } ], "majorSkills" : "Machine learning, Forecasting, Causal inference", "majorSkillsList" : [ "Machine learning", "Forecasting", "Causal inference" ], "coreResponsibilities" : "Lead data science workstream on client engagements, Set technical bar on machine learning, Set technical bar on forecasting, Build methods and templates, Mentor and grow data scientists, Mentor and grow data engineers, Shape new offerings, Represent Intellishore externally", "teamCollaboration" : "Client executives; Brand leadership; Leadership team", "domainKnowledge" : "Commercial pharma; Regulated industry; Healthcare; Pharma", "domainList" : [ "commercial-pharma", "regulated-industry", "healthcare", "pharma" ], "domainSpecificKnowledge" : [ "Commercial pharma", "Regulated industry", "Healthcare", "Pharma" ], "communicationSkills" : "Thought leadership; Client conversations", "specialRequirements" : "Right to work in Denmark", "qualificationsBasic" : "8+ years in data science, Machine learning experience, Forecasting experience, Commercial context experience, Marketing context experience, Pricing context experience, Lead senior client-facing engagements, Lead business-critical internal programs, Domain grounding in pharma, Domain grounding in healthcare, Domain grounding in regulated industry", "qualificationsPreferred" : "Understanding MLOps, Experience shaping data offerings, Experience shaping AI offerings, Experience launching data offerings, Experience launching AI offerings, Experience shaping data products, Experience shaping AI products, Experience launching data products, Experience launching AI products, Danish language", "remoteFriendly" : false, "workSiteFlexibility" : "Hybrid", "rawDescription" : "The problem we're hiring you to solve Commercial pharma is sitting on a measurement problem. Launch decisions, field force deployment, HCP engagement, pricing - most of the value moves before anyone knows what worked, and the standard playbooks weren't built for fragmented channels, privacy constraints, or the causal questions that actually matter to commercial leaders. And the ground keeps moving - AI is rewriting what \"analytics\" even means to a commercial org, and the regulatory and tech stack underneath it all is shifting faster than most teams can absorb. Intellishore works at the sharp end of this. Our commercial pharma practice partners with global top-20 pharma pioneers on data and AI decisions that shape launches, portfolios, and commercial performance. We're hiring a Principal Data Scientist to lead the technical depth of this work — shaping methodology, owning delivery on our most demanding engagements, and building out how we deliver measurements, causal inference, and machine learning in the next generation of our offerings. What you'll actually do Lead the data science workstream on senior client engagements - typically 1-2 concurrent projects, working directly with client executives and brand leadership. Set the technical bar on machine learning and forecasting across our commercial pharma portfolio and build the methods and templates that scale across the practice and into new segments. Mentor and grow a small team of data scientists and engineers. Team size: 10 today, growing to 30. Shape new offerings alongside our leadership - from scoping to pricing to the IP that underpins them. Represent Intellishore externally: Thought leadership, conferences, and senior client conversations where credibility matters. Who you are Must-haves: 8+ years in data science, with meaningful time spent on machine learning and forecasting in commercial, marketing, or pricing contexts. Track record of leading senior client-facing engagements or business-critical internal programs - consulting, in-house commercial analytics, or both. Strong domain grounding in pharma, healthcare, or another regulated industry where commercial measurement is hard. Nice to have: Understanding MLOps at a level where you know what good looks like and can still get your hands dirty when it matters. Experience shaping or launching data/AI offerings, practices, or products. Danish is a plus, not a requirement. Our working language is English. Why Intellishore? We're a boutique of ~100 people across Copenhagen and Zürich, and our commercial pharma stronghold is genuine - not a side bet. That means three things that matter at your level: Real mandate. You'll own the technical direction of a practice, not a single workstream within a larger machine. If you've wanted to shape how a firm approaches machine learning in healthcare, this is the seat. Concrete path. Practice Lead for ML in Commercial Pharma within 1–2 years; Director track over 2–4 years from hire. We'll map it to your ambitions in the first conversation. Compensation. We'll be direct about level, bonus structure, and the full package early in the process. Practicalities Based in Copenhagen. Expect 3-5 days in the office or at the client site. Travel roughly 5% of weeks, primarily within Europe. You must have the right to work in Denmark at the time of application. Apply We read every application. If most of this resonates, but you don't tick every box, apply anyway — we'd rather have the conversation. Reach out to Sofie Freund with questions: sofie.freund@intellishore.dk · +45 93 98 17 27. We interview on a rolling basis.", "summaryDescription" : "Principal Data Scientist at Intellishore. Skills: Machine learning, Forecasting, Causal inference. Lead data science workstream on client engagements. Set technical bar on machine learning", "postedDate" : "2026-06-14T20:09:49.596", "createdAt" : "2026-06-14T20:09:49.596", "lastUpdated" : "2026-06-16T00:46:31.765", "ingestTimestamp" : "2026-06-14T20:09:49.596", "mongoId" : "6a2ca5bd74d8ee00a09b7ee8", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Machine learning", "Forecasting", "Causal inference", "Measurement", "Client engagements", "Business critical programs", "Commercial measurement", "Data science workstream", "Methodology development", "Offering development", "Commercial pharma", "Regulated industry", "Healthcare", "Pharma" ], "skillCount" : 14, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 1.0, "_score" : 981.199, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Biotech", "llmIndustryConfidence" : 0.95 }, { "id" : "68b706987fc9a711f0f6fd2e", "title" : "Principal Applied Scientist", "company" : "Amazon Development Center U.S., Inc.", "industry" : "Data Science, Applied Science, Cloud Computing", "salary" : { "min" : 179000.0, "max" : 309400.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Seattle, Washington, USA", "locations" : [ "Seattle, Washington, USA" ], "timestamp" : "2026-06-15T02:15:48.309", "fingerprint" : "ee5be20183b9084bfd8649f55be2c1035dfda061915f2779e3ca2b22f40ec819", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3052445/apply", "externalId" : "837843be-4907-4b0f-b91e-9e829b40bcd8", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Seattle", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.6038321,-122.3300620" } ], "locationText" : "Seattle, Washington, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "AI, ML, NLP, Computer vision, Deep learning, AWS, Amazon Lex, Amazon Polly, AWS Lambda, Amazon S3, Amazon Kinesis", "professionalSkills" : [ "Scientific vision", "Business applications", "Turnkey solutions", "Customer impact", "Responsible AI" ], "hardSkillsList" : [ "Machine learning", "AI systems", "Scientific research", "Data science" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Machine learning", "level" : "intermediate" }, { "name" : "AI systems", "level" : "intermediate" }, { "name" : "Scientific research", "level" : "intermediate" }, { "name" : "Data science", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "AI, Machine Learning, Scientific vision", "majorSkillsList" : [ "AI", "Machine Learning", "Scientific vision" ], "coreResponsibilities" : "Drive scientific vision, Advise technical leadership, Initiate design of science solutions, Lead design of science solutions, Deliver science solutions, Propose innovative solutions, Influence organization adoption, Mentor scientists, Develop scientists", "teamCollaboration" : "Senior technical leaders; Across AWS; Product teams; Engineering teams", "domainKnowledge" : "Contact center; Cloud-based contact center; Artificial Intelligence; AWS services", "domainList" : [ "contact-center", "cloud-based-contact-center", "artificial-intelligence", "aws-services" ], "domainSpecificKnowledge" : [ "Contact center", "Cloud-based contact center", "Artificial Intelligence", "AWS services" ], "communicationSkills" : "Influence across organizations", "innovationCreativity" : "Rapid innovation; Innovative solutions", "problemSolvingSkills" : "Exceptional problem-solving; Analytical skills; Decision making; Ambiguous problem spaces", "outcomesObjectives" : "Deliver measurable customer impact", "qualificationsBasic" : "PhD in Computer Science, 10+ years research experience, Production AI/ML systems delivery, Deep expertise in AI/ML, Publication record, Experience leading teams, Exceptional problem-solving skills, Exceptional analytical skills", "qualificationsPreferred" : "Experience with large-scale ML systems, Experience with cloud computing, Track record of driving scientific innovation, Experience partnering with product teams, Experience partnering with engineering teams, Translate complex scientific concepts", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. The Principal Applied Scientist will drive the scientific vision for AI systems. The role requires tackling intrinsically hard, previously unsolved problems while balancing rapid innovation with reliability and compliance. You should be comfortable making decisions with incomplete information and driving solutions in ambiguous problem spaces. You'll work closely with senior technical leaders within the team and across AWS. We're looking for scientists who can maintain high standards while moving quickly, prioritizing both rapid experimentation and responsible AI development to deliver measurable customer impact. Key job responsibilities * Drive the scientific vision of teams in the organization and advise technical leadership on scientific challenges * Initiate and lead the design and delivery of science solutions that address significantly large or endemic customer and business problems * Propose innovative solutions to intrinsically hard, previously unsolved problems * Influence the organization towards adopting new approaches, concepts, and paradigms * Mentor and develop other scientists, fostering a culture of scientific excellence About the team Amazon Connect is a highly disruptive cloud-based contact center that enables businesses to deliver engaging, dynamic, and personal customer service experiences. Amazon Connect is the result of the ten years of development that went into building the tools Amazon uses to provide its award winning customer service at massive scale and launching it as a publicly available AWS service. With Amazon Connect, you can create your own cloud-based contact center and be taking calls in minutes. Amazon Connect leverages the power of Artificial Intelligence and the large ecosystem of AWS services such as Amazon Lex, Amazon Polly, AWS Lambda, Amazon S3 and Amazon Kinesis to provide a truly frustration free, easy-to-use, extensible, and natural customer experience. With this technology, we are transforming an industry and the way customers interact with businesses and how agents service them. Basic Qualifications: * PhD in Computer Science, Machine Learning, or related field * 10+ years of relevant, broad research experience after PhD * Track record of delivering production AI/ML systems with significant customer impact * Deep expertise in one or more areas of AI/ML (e.g., NLP, computer vision, deep learning) * Strong publication record in top-tier conferences or journals * Experience leading and mentoring teams of scientists and engineers * Exceptional problem-solving and analytical skills * Strong communication skills and ability to influence across organizations Preferred Qualifications: * Experience with large-scale machine learning systems and cloud computing * Track record of driving scientific innovation in industry settings * Experience partnering with product and engineering teams to deliver AI solutions * Demonstrated ability to translate complex scientific concepts to non-technical stakeholders Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.", "summaryDescription" : "Principal Applied Scientist at Amazon Development Center U.S., Inc.. Skills: AI, Machine Learning, Scientific vision. Drive scientific vision. Advise technical leadership", "postedDate" : "2026-06-13T07:25:16.970", "createdAt" : "2026-06-13T07:25:16.970", "lastUpdated" : "2026-06-15T02:15:48.309", "ingestTimestamp" : "2026-06-13T07:25:16.970", "mongoId" : "68b706987fc9a711f0f6fd2e", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3052445/apply", "salaryAnnualMin" : 179000, "salaryAnnualMax" : 309400, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Machine learning", "AI systems", "Scientific research", "Data science", "Leadership", "Mentoring", "AI", "Machine Learning", "Scientific vision", "Business applications", "Turnkey solutions", "Customer impact", "Responsible AI", "Contact center", "Cloud-based contact center", "Artificial Intelligence", "AWS services" ], "skillCount" : 17, "jobCategory" : "data", "dataQualityScore" : 1.0, "_score" : 925.1163, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology" }, { "id" : "6a306f13f292fd229cfe4e3e", "title" : "Sr Principal Data Scientist", "company" : "Smartsheet", "industry" : "Technology", "salary" : { "min" : 6000000.0, "max" : 9000000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Sr Principal Data Scientist in India, adjusted for executive seniority and SaaS industry." }, "location" : "India", "locations" : [ "India" ], "timestamp" : "2026-06-16T12:57:10.373", "fingerprint" : "a47fdd84f1abdd9a6c6bad7c3ae31c23d9dd31bbb39de2f4c75ee09115a7ad98", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/smartsheet/jobs/8007345", "externalId" : "8007345", "industryFunction" : { }, "structuredLocations" : [ { "country" : "India", "continent" : "as", "workMode" : "onsite" } ], "locationText" : "India", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "remoteFriendly" : false, "rawDescription" : "For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Smartsheet is looking for a Sr. Principal Data Scientist to set the technical direction for the ML models and AI sub-agents that drive growth, monetization, efficiency, and retention across the customer lifecycle. You’ll architect the systems, define the modeling and evaluation standards, and shape the roadmap for how Smartsheet uses applied ML and agentic AI to serve millions of customers. The data is unusually rich: petabyte-scale execution data spanning two decades of how real work gets done. You are a recognized technical leader who moves fluidly between architecture, prototype, and production; raises the bar for the data scientists around you; and partners with senior product and engineering leaders to make decisions that compound across the org. You will work primarily with Product and Engineering and will be a part of Smartsheet’s Business Intelligence team. This full-time position reports to the Director of Data Science and is based in Smartsheet’s Bengaluru, India office. You Will: Set the technical direction for how Smartsheet uses applied ML and agentic AI across the customer lifecycle — what gets built, what doesn’t, and what good looks like Architect the systems behind the sub-agents: how they ground themselves in evidence, how risk and confidence are calibrated, how decisions are evaluated, and how all of it stays safe and trustworthy at scale Define the modeling, evaluation, and experimentation standards the team follows — from offline metrics to online rollout to production monitoring Personally ship the highest-leverage models and sub-agents — the ones that need a senior IC to frame the problem, push through ambiguity, and de-risk the approach Drive technical decisions on the data foundations and knowledge layer sub-agents reason over, with strong stewardship of privacy, aggregation, and customer trust Partner with senior Product, Engineering, and Applied AI leaders to shape strategy, roadmap, and investment Raise the bar for the data science team — mentor staff and principal data scientists, review designs, set hiring standards, and grow the org’s modeling rigor You Have: Bachelor’s degree and 12+ years of experience (or 14+ years of experience); advanced degree in a quantitative field (Statistics, CS, ML, Economics, Operations Research, or similar) strongly preferred Track record of setting technical direction for applied ML and AI work that ships and matters — major initiatives, complex systems, or new modeling paradigms taken from idea to production impact Deep applied ML expertise across both traditional ML and deep learning: gradient boosting, regularized linear models, transformer-based sequence models, foundation model embeddings, causal ML, contextual bandits, and offline RL Architect-level grasp of agentic AI systems: tool use, retrieval, multi-step reasoning, evaluation, guardrails, and the patterns for keeping all of it reliable in production Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control Solid foundation in statistics and experimental design at scale: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods Experience operating ML in production at scale — feature engineering and pipelines, model monitoring, drift detection, retraining cadence, and the trade-offs between batch and real-time serving Proficient in SQL and Python; comfort with ML/LLM tooling at scale (Spark, Databricks, Snowflake, or equivalents), ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM), and visualization tools (Tableau or similar) Experience leading lifecycle modeling work — churn, expansion, adoption, plan health, lead/account scoring — and business fluency in the SaaS metrics that drive it (NRR, GRR, ARR, and cohort economics) A pragmatic production bar: latency, cost, monitoring, drift, hallucination, and what happens when the model or sub-agent is wrong Demonstrated ability to influence senior product and engineering leaders and to mentor staff and principal data scientists — your track record shows people and decisions, not just models, getting better Comfort operating in deep ambiguity — defining the problem, choosing the approach, and aligning the team when no playbook exists Get to Know Us: At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together. Equal Opportunity Employer: Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, and India. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. 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"AWS", "Azure", "GCP" ], "communicationSkills" : "Influence senior stakeholders", "continuousLearning" : "Continuous learning; Professional growth", "innovationCreativity" : "Innovative thinking; Creative thinking", "problemSolvingSkills" : "First-principles problem solving; Problem-solving; Decision-making", "outcomesObjectives" : "Improve operational efficiency; Improve reliability; Deliver actionable insights; Ensure maximum efficiency; Ensure reliability", "qualificationsBasic" : "5+ years of hands-on experience developing and deploying ML models, Hands-on experience with Python, Hands-on experience with TensorFlow/PyTorch, Hands-on experience with Scikit-learn, Hands-on experience with SQL, Hands-on experience with cloud ML platforms, Bachelor’s or higher in Chemical Engineering, Mechanical Engineering, Aerospace, or Applied Physics, Significant experience in heavy industry (for CS degrees), Background in handling time-series and sensor data", "qualificationsPreferred" : "PhD preferred, GCP Professional Data Engineer certification, AWS Data Analytics certification, Databricks Certified certification, Dbt Certified certification", "remoteFriendly" : true, "workSiteFlexibility" : "Hybrid", "rawDescription" : "About UptimeAI: UptimeAI is leading the way in predictive analytics and AI-driven solutions to optimize operational uptime and reduce downtime for industrial and enterprise clients. Our innovative platform harnesses cutting-edge data science to deliver actionable insights, ensuring maximum efficiency and reliability. UptimeAI uniquely combines Artificial Intelligence with Subject Matter Knowledge from 200+ years of cumulative experience to explain interrelations across upstream/downstream equipment, adapt to changes, identify problems, and give prescriptive diagnosis like a human expert would. Recently receiving a funding round led by WestBridge Capital, UptimeAI is prime for global expansion Role : Lead / Principal Data Scientist Location: Bangalore- Karnataka (Hybrid) Experience: 5+ years Department: Data Science ROLE OVERVIEW We are looking for individuals who sit at the intersection of Data Science and Physical Sciences. You understand that industrial data isn't just rows in a database—it represents thermodynamics, fluid dynamics, and mechanical stress. The candidate should be capable of translating business problems into scalable AI solutions, leveraging creative thinking and first-principles problem solving. This role requires someone who can: · Build and deploy production-grade AI systems, not just prototypes · Design agentic workflows that can reason, adapt, and take actions in dynamic environments · Work with noisy, incomplete, and real-time industrial data to derive meaningful insights · Collaborate closely with domain experts (e.g., operations, reliability, or plant engineers) to create context-aware AI solutions · Demonstrate innovative thinking in solving ambiguous problems where standard approaches may not apply KEY RESPONSIBILITIES · Be a part of the design, development, and deployment of advanced AI/ML models for predictive maintenance, process optimization, and industrial performance intelligence · Collaborate closely with Product, Engineering, and Customer Success teams to translate business problems from industrial domains into data science solutions. · Guide the team in handling complex, high-dimensional, and time-series sensor data from IoT, SCADA, and DCS systems. · Drive model interpretability, scalability, and accuracy, ensuring robust performance in real-world production environments. · Hybrid Modeling: Integrate First-Principles Models (mass/energy balances, kinetics) with data-driven ML to ensure physical consistency in model outputs. · Signal Processing: Lead the strategy for feature engineering on high-frequency telemetry data from SCADA/DCS, focusing on transient state detection and steady-state identification. · Operationalization: Move beyond \"notebook AI\" to deploy robust, low-latency inference pipelines that respect the edge-computing constraints of industrial environments. · Partner with industry experts and customers to validate models, derive insights, and deliver measurable business outcomes. · Stay ahead of emerging trends in AI/ML, deep learning, and industrial AI to incorporate innovative techniques into UptimeAi’s product roadmap. · Contribute to thought leadership by publishing whitepapers, patents, or presenting at industry conferences · Mentor and provide technical leadership to data scientists, ensuring continuous upskilling and adoption of best practices QUALIFICATIONS · Bachelor’s or higher in Chemical Engineering, Mechanical Engineering, Aerospace, or Applied Physics with a heavy emphasis on computational modeling. (Candidates with CS degrees are welcome if they have significant experience in heavy industry). · 5+ years of hands-on experience developing and deploying ML models specifically within Manufacturing, Energy, Oil & Gas, or Power sectors. · Strong background in handling time-series and sensor data from industrial or IoT systems. · Hands-on experience with Python, TensorFlow/PyTorch, Scikit-learn, SQL, and cloud ML platforms (AWS, Azure, GCP). · Excellent problem-solving and communication skills with the ability to influence senior stakeholders. · Demonstrated ability to lead and mentor teams while being a hands-on contributor · Ability to work independently, with strong problem-solving and decision-making abilities Why to join UptimeAI: · Impact Industry-Wide Change: Contribute to transformative solutions that significantly improve operational efficiency and reliability for global clients. · Collaborative and Growth-Oriented Environment: Join a talented, passionate team that values innovation, continuous learning, and professional growth. · Opportunities for Leadership and Innovation: Lead pioneering projects, influence product development, and shape the future of industrial AI solutions.", "summaryDescription" : "Lead / Principal Data Scientist at UptimeAI. Skills: AI, ML, Data Science, Industrial AI. Build production-grade AI systems. Deploy production-grade AI systems", "createdAt" : "2026-06-14T19:29:55.730", "lastUpdated" : "2026-06-16T03:31:32.901", "ingestTimestamp" : "2026-06-14T19:29:55.730", "mongoId" : "6a2c8d5d74d8ee00a0978bc6", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Predictive maintenance", "Process optimization", "Industrial performance intelligence", "Time-series analysis", "Sensor data analysis", "Telemetry data analysis", "Transient state detection", "Steady-state identification", "Inference pipelines", "Hybrid modeling", "Mass balances", "Energy balances", "Kinetics", "Leadership", "Mentoring", "AI", "ML", "Data Science", "Industrial AI", "Computational modeling", "First-principles modeling", "Signal processing", "Feature engineering", "Model interpretability", "Model scalability", "Model accuracy", "Problem-solving", "Decision-making", "Industrial data", "Thermodynamics", "Fluid dynamics", "Mechanical stress", "Manufacturing", "Energy sector", "Oil & Gas", "Power sectors", "Industrial systems", "IoT systems", "SCADA systems", "DCS systems", "GCP Professional Data Engineer", "AWS Data Analytics", "Databricks Certified", "Dbt Certified", "TensorFlow", "PyTorch", "Scikit-learn", "AWS", "Azure", "GCP" ], "skillCount" : 50, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Lead", "dataQualityScore" : 0.8888889, "_score" : 881.88806, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Manufacturing", "llmIndustryConfidence" : 0.98 }, { "id" : "6a305a6af292fd229cf50547", "title" : "Principal Data Scientist - Agent Builder", "company" : "Elastic", "industry" : "Technology", "salary" : { "min" : 140000.0, "max" : 210000.0, "currency" : "EUR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Principal Data Scientist in the Technology industry in the Netherlands, adjusted for executive seniority and company size." }, "location" : "Netherlands", "locations" : [ "Netherlands" ], "timestamp" : "2026-06-16T11:05:30.121", "fingerprint" : "8e027fce54abc926a9eebea78a61fc140b324b454e895a1c094d41cc76a8efd9", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://jobs.elastic.co/jobs?gh_jid=8008494&gh_jid=8008494", "externalId" : "8008494", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Netherlands", "continent" : "eu", "workMode" : "onsite" } ], "locationText" : "Netherlands", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "technicalSkills" : "Python, SQL, Spark, AWS, GCP, Azure, Snowflake, BigQuery, Redshift, Databricks, Dbt, Fivetran, Airflow, Delta Lake, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Hugging Face, MLflow, SageMaker, Vertex AI, ML Studio, S3, Glue, Athena, Synapse", "professionalSkills" : [ "Data engineering", "ML engineering", "Business intelligence", "Analytics engineering", "Quantitative analysis", "Statistical modeling", "Machine learning", "Deep learning", "Data modeling", "Data warehousing", "ETL development", "Data pipeline", "Data visualization", "Experiment design", "A/B testing", "Causal inference" ], "hardSkillsList" : [ "Statistical analysis", "Regression analysis", "Classification models", "Clustering algorithms", "Time-series forecasting", "Experimentation design", "Causal inference methods", "Model deployment", "Model monitoring", "Data wrangling", "Feature engineering", "Algorithm development" ], "hardSkills" : [ { "name" : "Statistical analysis", "level" : "intermediate" }, { "name" : "Regression analysis", "level" : "intermediate" }, { "name" : "Classification models", "level" : "intermediate" }, { "name" : "Clustering algorithms", "level" : "intermediate" }, { "name" : "Time-series forecasting", "level" : "intermediate" }, { "name" : "Experimentation design", "level" : "intermediate" }, { "name" : "Causal inference methods", "level" : "intermediate" }, { "name" : "Model deployment", "level" : "intermediate" }, { "name" : "Model monitoring", "level" : "intermediate" }, { "name" : "Data wrangling", "level" : "intermediate" }, { "name" : "Feature engineering", "level" : "intermediate" }, { "name" : "Algorithm development", "level" : "intermediate" } ], "majorSkills" : "Data science, Machine learning, Data engineering, AI", "majorSkillsList" : [ "Data science", "Machine learning", "Data engineering", "AI" ], "coreResponsibilities" : "Build and deploy machine learning models, Develop and maintain data pipelines, Design and implement data solutions, Conduct data analysis, Perform statistical modeling, Develop ML models, Deploy ML models, Monitor ML models, Collaborate with engineering teams, Collaborate with product teams, Drive data science initiatives, Contribute to data strategy, Build agent models, Develop AI agents", "teamCollaboration" : "Cross-functional teams; Engineering teams; Product teams", "toolProficiency" : [ "Python", "SQL", "Spark", "AWS", "GCP", "Azure", "Snowflake", "BigQuery", "Redshift", "Databricks", "Dbt", "Fivetran", "Airflow", "Delta Lake", "Scikit-learn", "TensorFlow", "PyTorch", "XGBoost", "LightGBM", "Hugging Face", "MLflow", "SageMaker", "Vertex AI", "ML Studio", "S3", "Glue", "Athena", "Synapse" ], "problemSolvingSkills" : "Root cause analysis; Troubleshooting; Data-driven decision making", "remoteFriendly" : true, "rawDescription" : "Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Search Conversational Experiences team builds Elastic’s new conversational and agentic platform that lets customers chat with their own data in Elasticsearch. We build the core quality layer for RAG, agents and tools, retrieval and citations, streaming, memory, and the evaluation signals that turn open-ended questions into grounded, reliable answers. As a Principal Data Scientist, you will help set the technical direction for how we evaluate, improve, and scale chat quality across Elastic’s agentic platform. You will define the evaluation strategy that guides product decisions, including which models we standardize on, how we route requests across agents, which tools we enable and when, and how we tailor agents to different Elastic use cases in search and beyond. You will work closely with backend engineering, product, UX, and other data scientists to turn ambiguous, cutting-edge problems into measurable product improvements. You’ll help lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to improve groundedness, shaping reasoning strategies and tool-selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality where it can create meaningful user value. This is an applied leadership role: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel. What You Will Be Doing Define the evaluation strategy for conversational and agentic search, including offline and online evaluation, golden datasets, rubrics, LLM-as-judge calibration, groundedness and citation checks, and A/B testing. Lead the design of quality metrics and decision frameworks for RAG, agents, tools, model selection, agent routing, prompt behavior, and cost/latency trade-offs. Build, compare, and guide improvements across retrieval and re-ranking approaches, including sparse and dense retrieval, vector search, query understanding, semantic rewrites, and context enrichment. Turn experimental results into product and business decisions: which models to use, how to route requests efficiently, which tools should be exposed, and how agents should be customized for different Elastic use cases. Partner with engineering to productionize evaluation pipelines, telemetry, dashboards, CI guardrails, and regression detection for chat quality, helpfulness, dedication, latency, and cost. Influence the roadmap by identifying the highest-leverage quality gaps, proposing practical solutions, and communicating trade-offs clearly to product, engineering, and leadership. Mentor other data scientists and engineers in experiment design, evaluation methodology, statistical rigor, and practical approaches to improving LLM-powered systems. Share outcomes through clear docs, notebooks, PRs, dashboards, technical proposals, and cross-functional reviews. What You Bring 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences. Strong track record defining and leading evaluation for production AI/ML systems, including offline metrics, online experimentation, LLM-as-judge approaches, groundedness, citation quality, and model comparison. Experience influencing product and technical strategy through data, especially in ambiguous or emerging domains where the “right” metric or approach is not obvious at the start. Hands-on ability with Python, PyTorch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code. Strong understanding of retrieval systems, including dense and sparse retrieval, re-ranking, vector search, query understanding, and evaluation metrics such as nDCG, MRR, Recall@k, precision,and latency/cost trade-offs. Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking. Practical Elasticsearch experience, or experience with similar search and distributed data systems. ES|QL familiarity is a plus. Excellent written and verbal communication, with the ability to explain complex scientific and technical trade-offs to engineering, product, design, and leadership audiences. A collaborative, low-ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team. Compensation for this role is in the form of base salary. This role does not have a variable compensation component. At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets. The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position. The typical starting salary range for this role is: €80.400—€127.200 EUR Additional Information - We Take Care of Our People As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. Competitive pay based on the work you do here and not your previous salary Health coverage for you and your family in many locations Ability to craft your calendar with flexible locations and schedules for many roles Generous number of vacation days each year Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service Up to 40 hours each year to use toward volunteer projects you love Embracing parenthood with minimum of 16 weeks of parental leave Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation. We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission. Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster) Elasticsearch develops and distributes technology and information that is subject to U. S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic. Please see here for our Privacy Statement.", "summaryDescription" : "Principal Data Scientist - Agent Builder at Elastic. Skills: Data science, Machine learning, Data engineering, AI. Build and deploy machine learning models. Develop and maintain data pipelines", "postedDate" : "2026-06-15T23:22:17.099", "createdAt" : "2026-06-15T23:22:17.099", "lastUpdated" : "2026-06-16T11:05:30.121", "ingestTimestamp" : "2026-06-15T23:22:17.099", "mongoId" : "6a305a6af292fd229cf50547", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "100k-150k", "hasSalary" : false, "allSkills" : [ "Statistical analysis", "Regression analysis", "Classification models", "Clustering algorithms", "Time-series forecasting", "Experimentation design", "Causal inference methods", "Model deployment", "Model monitoring", "Data wrangling", "Feature engineering", "Algorithm development", "Data science", "Machine learning", "Data engineering", "AI", "ML engineering", "Business intelligence", "Analytics engineering", "Quantitative analysis", "Statistical modeling", "Deep learning", "Data modeling", "Data warehousing", "ETL development", "Data pipeline", "Data visualization", "Experiment design", "A/B testing", "Causal inference", "Python", "SQL", "Spark", "AWS", "GCP", "Azure", "Snowflake", "BigQuery", "Redshift", "Databricks", "Dbt", "Fivetran", "Airflow", "Delta Lake", "Scikit-learn", "TensorFlow", "PyTorch", "XGBoost", "LightGBM", "Hugging Face", "MLflow", "SageMaker", "Vertex AI", "ML Studio", "S3", "Glue", "Athena", "Synapse" ], "skillCount" : 58, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 863.0475, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2cd40374d8ee00a0a37cda", "title" : "Engineering Principal", "company" : "HDR", "industry" : "Consulting", "salary" : { "min" : 142296.0, "max" : 203280.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "United States-Minnesota-Saint Louis Park", "locations" : [ "United States-Minnesota-Saint Louis Park" ], "timestamp" : "2026-06-15T06:51:24.143", "fingerprint" : "1c1d4ee664b5effbca20a5c169afe0b666a6f4dc256008b65e1b08f7bff2a7c0", "source" : "taleo", "language" : "en", "applyUrl" : "https://hdr.taleo.net/careersection/ex/jobdetail.ftl?job=783803", "externalId" : "783803", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Saint Louis Park", "state" : "Minnesota", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "44.9475726,-93.3569023" } ], "locationText" : "Saint Louis Park, Minnesota, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "professionalSkills" : [ "Leadership", "Communication", "Business development", "Project delivery", "Design challenges", "Integrated practice" ], "hardSkillsList" : [ "Engineering expertise", "Business development", "Scope development", "Schedule development", "Fee development", "Design process", "Team leadership" ], "softSkillsList" : [ "Leadership" ], "hardSkills" : [ { "name" : "Engineering expertise", "level" : "intermediate" }, { "name" : "Business development", "level" : "intermediate" }, { "name" : "Scope development", "level" : "intermediate" }, { "name" : "Schedule development", "level" : "intermediate" }, { "name" : "Fee development", "level" : "intermediate" }, { "name" : "Design process", "level" : "intermediate" }, { "name" : "Team leadership", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" } ], "majorSkills" : "Engineering leadership, Business development, Project delivery", "majorSkillsList" : [ "Engineering leadership", "Business development", "Project delivery" ], "coreResponsibilities" : "Influence culture, morale, recruiting, Provide leadership, Identify opportunities, Take initiative, Establish relationships, Promote and represent BES, Develop scope, schedule, fee, Communicate pursuits, Address staffing needs, Address expertise requirements, Address business needs, Identify how to engage people, Align with strategic goals, Facilitate clear communication, Provide leadership for design team, Engage and challenge designers, Lead engineering teams, Deliver well-coordinated designs, Provide leadership for discipline, Act as leaders in practice, Maximize high performance design, Proactively look out for disciplines, Elevate design, Ensure scope, schedule, budget, Communicate concerns to management, Protect client relationships", "teamCollaboration" : "Cross discipline teams; Integrated practice; Design team; Project teams; Internal clients; External clients", "projectManagementSkills" : [ "Scope management", "Schedule management", "Budget management" ], "domainKnowledge" : "Building engineering; Data centers; Commercial environments; Built environment", "domainList" : [ "building-engineering", "data-centers", "commercial-environments", "built-environment" ], "domainSpecificKnowledge" : [ "Building engineering", "Data centers", "Commercial environments", "Built environment" ], "communicationSkills" : "Client communication; Internal communication; External communication", "continuousLearning" : "Tuition reimbursement", "innovationCreativity" : "Novel ideas; Innovation", "problemSolvingSkills" : "Complex design challenges", "outcomesObjectives" : "Win work for HDR; Help partner BGs win work; Capture building engineering scope; Deliver high-quality designs", "qualificationsBasic" : "Deep engineering expertise, Understand cross discipline teams, Business development skills, Develop scope, schedule, fee, Communicate pursuits with leadership, Understand building design process, Conceptual thinker, Engage and challenge designers, Lead engineering teams, Elevate design, Ensure scope, schedule, budget", "qualificationsPreferred" : "SME, Participate in strategy/visioning sessions", "remoteFriendly" : false, "rawDescription" : "At HDR, our employee-owners are fully engaged in creating a welcoming environment where each of us is valued and respected, a place where everyone is empowered to bring their authentic selves and novel ideas to work every day. As we foster a culture of inclusion throughout our company and within our communities, we constantly ask ourselves: What is our impact on the world? Watch Our Story: ' https://www.hdrinc.com/our-story' Each and every role throughout our organization makes a difference in our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your community, but around the world. We believe building engineering is more than systems and structures, it’s about powering progress and enabling innovation. As part of HDR’s Building Engineering Services Group, you’ll help design the critical infrastructure that supports the digital age and shapes the spaces where people work, connect, and thrive. From high-performance data centers driving the future of AI to dynamic commercial environments, your work will directly support technological advancement, sustainability, and human experience. We bring together mechanical, electrical, structural, and civil engineering experts to solve complex design challenges with precision, creativity, and collaboration. Whether you're optimizing energy efficiency, integrating resilient systems, or engineering the backbone of next-generation facilities, your contributions will help define the future of the built environment. This isn’t just a job, it’s a chance to lead innovation, engineer impact, and build a legacy of excellence. Engineering Principal Engineering Principals are leaders in the practice who influence culture, morale, and recruiting as well as provide leadership in the pursuit and delivery of work. An Engineering Principal not only has deep engineering expertise in their discipline, but they understand how cross discipline teams’ function. They couple that understanding with leadership and communication abilities to positively impact the success of the business. The behaviors represented by Engineering Principals are indicative to our staff, clients, and recruits of the behaviors and characteristics HDR rewards. Winning Work Engineering Principals have a significant impact on winning work for HDR – both for the Building Engineering Services (BES) Business Group (BG) and partner BGs. They have business development skills to identify opportunities and take the initiative to be responsive and establish relationships to win work. They promote and represent BES to our internal and external clients – they foster relationships that make people want to work with us. There are three ways in which BES wins work and Engineering Principals contribute to each: Book work in BES market sectors. Collaborate with partner business groups to help them win work in their market sectors by developing and leveraging our expertise, relationships, and reputation. Capture building engineering scope of work on HDR non-BES projects by selling our services internally. Engineering Principals are expected to help develop scope, schedule, and fee for projects. They must communicate regarding the pursuits they are leading with operational leadership to address staffing, expertise requirements, and business needs. Engineering Principals help us identify how to engage the right people in the right work in alignment with our strategic goals: “differentiating ourselves in our markets by amplifying our collective talent and knowledge”. Project Success Engineering Principals understand the building design process in the market sectors in which they practice. They facilitate clear communication and provide leadership for the entire design team. An Engineering Principal needs to be a conceptual thinker who can engage and challenge our architectural designers and engineering team during the early stages of projects to inform design. They should leverage our integrated practice to add value to our project delivery process and lead engineering teams to deliver well-coordinated, high-quality designs to our clients. Engineering Principals are expected to not only provide leadership for their discipline but act more broadly as leaders in our practice. The role requires a passion for maximizing high performance design within the project constraints. They are the team members who proactively look out for all BES disciplines on project teams to elevate design while ensuring that scope, schedule, and budget are understood and respected by the team and concerns are communicated to project management in a clear and timely manner. Engineering Principals are protecting HDR’s Client relationships (internal and external) and project successes. Their leadership has a direct impact on HDR’s financial plan. Culture, Morale & Recruiting Engineering Principals are leaders in the company that exemplify the company culture. They demonstrate expectations of how project teams' function and emphasize collaboration and design excellence. These individuals are responsible for mentoring and building the next generation of leadership at HDR by developing technical, client and business acumen in others. Principals recognize gaps in our expertise and project teams. They identify ways to fill those gaps by mentoring junior staff, identifying where we can shift or train staff, or identifying where we need to hire additional expertise. Principals network in the local community – leveraging their relationships to identify talent and recruit for HDR. Engineers in this role are contributing to BES’s strategy to develop expertise and strategy required to build a successful engineering practice. Engineer Principals represent HDR through their participation in the professional and local community. They are expected to represent HDR in the engineering community, participate in speaking engagements, author white papers and technical publications and participate on industry and community boards. Clarification from SME or Senior Engineer The role of Engineering Principal is a progression beyond Senior Engineer or Subject Matter Expert. But this is not a recognition that all Senior Engineers can expect to earn with time as the next step on the ladder. Competencies are not solely based upon technical skills. Subject Matter Expert/ Senior Engineer Exemplary technical performance and expertise. Hold the resume required to win work. In-depth market sector expertise. Interview performance to win work. Mentor to junior staff. Execute projects with little to no direction. Many Engineering Principals will also be SMEs. Framework Report or meet regularly with Area BG Management (Business Group Manager, Professional Services Leader, Project Delivery Leader, etc.) Participate in strategy/visioning sessions Values from HDR Strategic Plan: Engineering Principals are expected to guide teams to see the bigger picture beyond the immediate problem or project in front of them. Influence people to align with our company values: Live the network. We think global and act local, and as a result we learn from each other. We bring together the right people for each individual client and engagement, e nsuring the right skills, expertise, experience and the highest level of teamwork. Every time. Listen first. To find the best answer, we need to understand the root and complexity of the problem. We start by listening. Hold ourselves accountable. We are empowered to find the best solutions for our clients and for our own company. We do the right things for the right reasons. And we take personal responsibility to see things through. Push boundaries. We strive to do things better and stretch further. From our internal processes to the work we deliver, and from the projects we do, to the careers we enable. Design smart. We have the skills and ability to tailor our approach to each situation, whether it’s tried and tested, or something more cutting edge. We deliver our best work by designing smart. We provide a comprehensive benefits package that promotes employee ownership, employee health, performance, and success, which includes medical, dental, vision, short and long-term disability, life insurance, an employee assistance program, paid time away, parental leave, paid holidays, a retirement savings plan with employer match, employee referral bonus and tuition reimbursement. The expected compensation range for this position depends upon skills, experience, education and geographical location. (Stated benefits are for full-time regular positions. Temporary and part-time roles eligible for limited benefits.) Minnesota: %24142,296 - %24203,280", "summaryDescription" : "Engineering Principal at HDR. Skills: Engineering leadership, Business development, Project delivery. Influence culture, morale, recruiting. Provide leadership", "createdAt" : "2026-06-14T06:26:48.873", "lastUpdated" : "2026-06-15T06:51:24.143", "ingestTimestamp" : "2026-06-14T06:26:48.873", "mongoId" : "6a2cd40374d8ee00a0a37cda", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 142296, "salaryAnnualMax" : 203280, "salaryBand" : "100k-150k", "hasSalary" : true, "allSkills" : [ "Engineering expertise", "Business development", "Scope development", "Schedule development", "Fee development", "Design process", "Team leadership", "Leadership", "Engineering leadership", "Project delivery", "Communication", "Design challenges", "Integrated practice", "Scope management", "Schedule management", "Budget management", "Building engineering", "Data centers", "Commercial environments", "Built environment" ], "skillCount" : 20, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 851.78406, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Engineering", "llmIndustryConfidence" : 0.95 }, { "id" : "6a2ca0ce74d8ee00a09a3594", "title" : "Staff / Principal Data Scientist", "company" : "Auror", "industry" : "Product", "salary" : { "min" : 157500.0, "max" : 225000.0, "currency" : "USD", "frequency" : "Yearly" }, "location" : "Remote - United States", "locations" : [ "Remote - United States" ], "timestamp" : "2026-06-15T06:21:48.680", "fingerprint" : "a3f3537d052566d251b3b04110945d1b0aab290d9a84544eed0a6d64c19e6bf0", "source" : "ashby", "language" : "en", "applyUrl" : "https://jobs.ashbyhq.com/auror/0f19a00b-ff17-400f-9d1e-79132dbf164b/application", "externalId" : "0f19a00b-ff17-400f-9d1e-79132dbf164b", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, SQL", "professionalSkills" : [ "Statistical analysis", "Causal inference", "Experimental design", "Model calibration", "Measurement", "Risk classification", "Shrink prediction", "Loss Prevention", "Fraud detection", "Asset Protection", "POS Analytics", "ETL Pipeline Design", "Data Modeling", "Data Architecture", "Responsible AI", "Geospatial risk modeling", "Network analytics", "Graph analytics", "Exception Based Reporting" ], "hardSkillsList" : [ "Statistical modeling", "Time series analysis", "Model calibration", "Causal inference", "Experimental thinking", "Risk classification", "Shrink prediction", "ETL pipeline design", "Data modeling", "Data architecture" ], "hardSkills" : [ { "name" : "Statistical modeling", "level" : "intermediate" }, { "name" : "Time series analysis", "level" : "intermediate" }, { "name" : "Model calibration", "level" : "intermediate" }, { "name" : "Causal inference", "level" : "intermediate" }, { "name" : "Experimental thinking", "level" : "intermediate" }, { "name" : "Risk classification", "level" : "intermediate" }, { "name" : "Shrink prediction", "level" : "intermediate" }, { "name" : "ETL pipeline design", "level" : "intermediate" }, { "name" : "Data modeling", "level" : "intermediate" }, { "name" : "Data architecture", "level" : "intermediate" } ], "majorSkills" : "Predictive modeling, Statistical modeling, Loss Prevention, Fraud detection", "majorSkillsList" : [ "Predictive modeling", "Statistical modeling", "Loss Prevention", "Fraud detection" ], "coreResponsibilities" : "Own predictive modeling end-to-end, Own advanced analytics end-to-end, Frame ambiguous problems, Deploy predictive models, Focus on LP applications, Focus on AP applications, Design ETL pipelines, Build ETL pipelines, Standardize ETL pipelines, Support scalable predictive modeling, Build defensible metrics, Link metrics to decision outcomes, Partner with ML engineers, Productionize feature definitions, Productionize scoring workflows, Productionize monitoring signals, Productionize retraining triggers, Communicate model uncertainty, Communicate model value, Communicate strategic application, Design randomized controlled trials, Design evaluation harnesses", "teamCollaboration" : "Partner with ML engineers; Communicate to diverse audiences; Communicate to internal Product/Engineering; Communicate to external customers; Communicate to executive teams", "domainKnowledge" : "Retail Loss Prevention; Fraud detection; Asset Protection; POS Analytics; Responsible AI", "domainList" : [ "retail-loss-prevention", "fraud-detection", "asset-protection", "pos-analytics", "responsible-ai" ], "domainSpecificKnowledge" : [ "Retail Loss Prevention", "Fraud detection", "Asset Protection", "POS Analytics", "Responsible AI" ], "communicationSkills" : "Communicate clearly; Communicate concisely", "continuousLearning" : "Personal growth support; Courses; Conferences; Events", "innovationCreativity" : "Embracing AI potential; Curious about AI", "problemSolvingSkills" : "Ambiguous problem framing; Statistical modeling; Advanced analytics", "qualificationsBasic" : "Expertise in statistical modeling, Experience taking models from problem to shipped decision support, Domain expertise in Retail Loss Prevention, SQL + Python experience, Experience in ETL Pipeline Design, Experience in Data Modeling & Architecture, Practical approach to responsible AI", "qualificationsPreferred" : "Experience with geospatial risk modeling, Experience with network/graph analytics, Experience with Exception Based Reporting tools, Experience building customer-facing proofs-of-concept, Translating complex analytics into product features", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "ABOUT AUROR At Auror, we’re empowering the retail industry to tackle theft and Organised Retail Crime, a $150 Billion problem globally. It’s high volume crime that’s increasingly organised in nature and is putting people, retailers, and communities at risk every day. Founded in New Zealand 12 years ago, we’re working with some of the best and largest retailers in the world across the US, Canada, Australia, New Zealand, and the UK. Auror is connecting people and intelligence to reduce crime. We’re using technology for good. Our mission is clear: reduce violent retail crime by 50% in 5 years. It's an ambitious goal - and one we believe is achievable. In partnership with our leading retail partners, we need people with the passion, determination, and innovation required to overcome one of the world's largest problems. If you’re looking to make a difference with and for the people dedicated to stopping crime, for good, then we want you on our team. We're also embracing the potential of AI to supercharge our impact - whether that's enhancing the way we detect trends, support our customers, or improve internal workflows. As a company, we're committed to responsibly incorporating AI into how we work and what we build, and we encourage all Aurors to be curious about how AI can elevate their work, regardless of role or function. THE ROLE Auror’s North American footprint is live and expanding. We need a highly practical and rigorous Data Scientist to build, productionize, and defend predictive models that customers can trust, especially for high-stakes operational decisions in Loss Prevention and Fraud detection (e.g., cashier risk scoring, deployment optimization, shrink prediction). This role fills a current need for deep statistical modeling expertise combined with real-world domain knowledge and the ability to ship robust analytics products. RESPONSIBILITIES - Own predictive modeling and advanced analytics end-to-end, from ambiguous problem framing to deployment, focusing on Loss Prevention (LP) and Asset Protection (AP) applications. - Design, build, and standardize ETL pipelines and data architecture necessary to support robust, scalable predictive modeling at scale. - Build defensible metrics (calibration, error tradeoffs, bias/disparity checks) and link them to measurable decision outcomes for C-suite and AP teams. - Partner with ML engineers to productionize: feature definitions, scoring workflows, monitoring signals, and retraining triggers. - Communicate clearly and concisely to diverse audiences (internal Product/Engineering, external customers, and executive teams) on model uncertainty, value, and strategic application. - Design randomized controlled trials and evaluation harnesses that make model quality legible to stakeholders and customers. REQUIREMENTS - Expertise in practical statistical modeling and analysis (inference, causal/experimental thinking, time series, model calibration, measurement) applied to real-world, messy data. - Demonstrated experience taking analyses/models from ambiguous problem → shipped decision support (e.g., shortage prediction, risk classification models). - Strong domain expertise in Retail Loss Prevention, Fraud detection, Asset Protection, or POS Analytics. - Strong SQL + Python, with demonstrated experience in ETL Pipeline Design and Data Modeling & Architecture. - Practical, high-integrity approach to responsible AI (limits, misuse risk, bias considerations, auditability). ⁠ NICE TO HAVES - Experience with geospatial risk modeling, network/graph analytics, or Exception Based Reporting (EBR) tools. - Experience building customer-facing proofs-of-concept and translating complex analytics into product features. - We are looking for people who demonstrate a strong alignment to our Guiding Principles (you can find these on our Careers page https://www.auror.co/company-careers). With diversity and inclusion at the forefront of Auror’s guiding principles, we promote a culture that celebrates diversity and inclusiveness at Auror, regardless of, but not limited to, race, gender, sexual orientation, family status, religion, ethnicity, national origin, physical disability, veteran status, or age. BENEFITS: - Competitive salary range: Depending on level of experience, between $[Salary band]. - Employee share scheme: You’ll own part of a company making a real difference! - Flexibility: We are hard-working and outcome focused, but recognise there is more to life than work. We promote a healthy work/life blend. - Shorter work weeks (at full pay): Everyone gets Friday afternoons off, so you can start your weekend early, and do more of whatever it is that makes you happy. - Focus on mental and physical health: We understand how vital our health is and have policies to support your wellness, including Wellness Days, and up to $500 for expert sessions every year. - Health care plan (Medical, Dental & Vision): Auror covers 100% of the cost of your individual health insurance plan with Anthem & Metlife. - Family-friendly: We offer comprehensive paid parental leave - 12 weeks for birth parents and 6 weeks for non-birth parents following birth, adoption, or surrogacy, available to all Aurors from day one. - Personal growth: We support our team to participate in courses, conferences, or events that will help them develop their skills. - Team love: We have regular team lunches and social events where most (if not all) activities are during work hours. What are the next steps? If you’re excited about our mission and you have the experience and a passion for this role, please hit “Apply”. If you’re not sure that you tick all the boxes but feel you’re close to what we’re looking for, please apply anyway! We’re proud that Auror is a place where everyone can learn and grow so we’d love to hear from you. You'll be asked to submit a cover letter as part of your application. While this is optional we do encourage it, as we find cover letters can tell a story that resumes alone often cannot. Our hiring team love to understand what attracted you to this role and why you are excited about the opportunity to join Auror. Once you apply, you’ll hear from us to acknowledge your application. If you have questions about any of the above, or if you have any accessibility requirements, we’ll be able to help you from there.", "summaryDescription" : "Staff / Principal Data Scientist at Auror. Skills: Predictive modeling, Statistical modeling, Loss Prevention, Fraud detection. Own predictive modeling end-to-end. 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Example: Professional Engineer (PE or P.Eng) license. Extensive experience in project execution Proficient computer skills in Microsoft Office as well as standard design software. Must be comfortable being viewed as a technical expert in engineering analysis and design and have proven background of technical leadership on these types of projects An attitude and commitment to being an active participant of our employee-owned culture What We Believe HDR is our company. Together, we build on each other's life experiences and perspectives to make great things possible every day. This shapes our collaborative culture, encourages organizational trust and connects us closer to the clients and communities we serve. Our Commitment As employee owners, we all have a role in creating an inclusive environment where each of us is welcomed, valued, respected and empowered to bring our authentic selves to work every day. Our eight Employee Network Groups (Asian Pacific, Black, Hispanic/Latino(a), LGBTQ+, People with Disabilities, Veterans, Women, Young Professionals) help create a sense of belonging and foster a supportive environment where everyone is empowered to engage and contribute. Each group has an executive sponsor and is open to all employees. United States-Minnesota-Saint Louis Park", "summaryDescription" : "Engineering Principal. Perform engineering analysis. Perform engineering design", "createdAt" : "2026-06-14T04:57:07.760", "lastUpdated" : "2026-06-15T09:22:26.977", "ingestTimestamp" : "2026-06-14T04:57:07.760", "mongoId" : "6a16291b58b93c861e959dd5", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 245000, "salaryAnnualMax" : 375000, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "Project execution", "Technical leadership", "Professional Engineer (PE or P. 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Define technical strategy, model development standards, and best practices across data science teams. Collaborate with engineering and product teams to design, build, and operationalize scalable AI solutions. Analyze large-scale datasets to identify meaningful patterns, trends, and opportunities for model improvement. Provide technical leadership and mentorship to junior and mid-level data scientists, ensuring high-quality outputs. Establish methodologies, frameworks, and governance standards for AI/ML development and deployment. Partner with stakeholders to align data science initiatives with business goals and customer needs. Communicate insights, technical findings, and recommendations clearly to both technical and non-technical audiences. Lead small to mid-sized teams, providing direction, structure, and execution oversight across projects. Continuously evaluate emerging AI and ML technologies to improve model performance and innovation. Requirements: 10+ years of experience in data science, AI, or machine learning roles with significant senior-level responsibility. Proven experience leading complex AI/ML projects and delivering production-grade solutions. Strong background in mentoring, coaching, and guiding technical teams in a leadership capacity. Deep expertise in machine learning techniques such as deep learning, gradient boosting, and ensemble methods. Strong experience with large language models and transformer architectures (e.g., BERT, T5, GPT-based systems). Solid understanding of prompt engineering, LLM behavior, and applied generative AI techniques. Experience working with large-scale datasets and distributed computing frameworks (e.g., Spark, Hadoop). Strong programming skills in Python and familiarity with modern ML frameworks. Excellent communication skills with the ability to influence senior stakeholders and cross-functional teams. Strong problem-solving mindset with the ability to navigate ambiguity and drive technical decisions. Advanced degree (Master’s or PhD) in Computer Science, Mathematics, Statistics, or a related field is highly preferred. Benefits: Competitive base salary range of $114,400 – $164,400 CAD, plus eligibility for annual bonus incentives. Comprehensive health, dental, and vision coverage through a multi-carrier plan. Retirement savings plans, including employer contributions and stock purchase options. Disability, life, and accident insurance coverage. Flexible and remote work arrangements supporting work-life balance. Family-focused benefits, including adoption and surrogacy support. Dependent care and commuter spending accounts. Inclusive and diverse workplace culture with strong commitment to equity and accessibility. ## Additional Information How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1", "summaryDescription" : "Principal Data Scientist. Skills: AI, Machine learning, Data science, Generative AI. Lead development and deployment of AI/ML models. Define technical strategy", "postedDate" : "2026-06-12T07:02:23.299", "createdAt" : "2026-06-12T07:02:23.299", "lastUpdated" : "2026-06-15T23:59:49.999", "ingestTimestamp" : "2026-06-14T22:14:25.653", "mongoId" : "6a2cb20e74d8ee00a09df743", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "100k-150k", "hasSalary" : false, "allSkills" : [ "Machine learning techniques", "Deep learning", "Gradient boosting", "Ensemble methods", "LLM behavior", "Applied generative AI", "Leadership", "Mentoring", "Problem-solving", "AI", "Machine learning", "Data science", "Generative AI", "Technical strategy", "Model development", "Production solutions", "Technical leadership", "Prompt engineering", "Execution oversight", "Large language models", "Transformer architectures", "Generative AI techniques" ], "skillCount" : 22, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 848.5625, "daysOld" : 3, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a305a6af292fd229cf5054e", "title" : "Principal Data Scientist - Agent Builder", "company" : "Elastic", "salary" : { "min" : 140000.0, "max" : 210000.0, "currency" : "EUR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Principal Data Scientist in the Technology industry in the Netherlands, adjusted for executive seniority and full-time employment." }, "location" : "Netherlands", "locations" : [ "Netherlands" ], "timestamp" : "2026-06-16T08:10:42.353", "fingerprint" : "e40db0762c43f936f11de5b8d1eb6f4b99a6c75a4a4443d9c56580ffb77bbe8e", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://jobs.elastic.co/jobs?gh_jid=8008502&gh_jid=8008502", "externalId" : "8008502", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Netherlands", "continent" : "eu", "workMode" : "onsite" } ], "locationText" : "Netherlands", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "remoteFriendly" : true, "rawDescription" : "Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Search Conversational Experiences team builds Elastic’s new conversational and agentic platform that lets customers chat with their own data in Elasticsearch. We build the core quality layer for RAG, agents and tools, retrieval and citations, streaming, memory, and the evaluation signals that turn open-ended questions into grounded, reliable answers. As a Principal Data Scientist, you will help set the technical direction for how we evaluate, improve, and scale chat quality across Elastic’s agentic platform. You will define the evaluation strategy that guides product decisions, including which models we standardize on, how we route requests across agents, which tools we enable and when, and how we tailor agents to different Elastic use cases in search and beyond. You will work closely with backend engineering, product, UX, and other data scientists to turn ambiguous, cutting-edge problems into measurable product improvements. You’ll help lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to improve groundedness, shaping reasoning strategies and tool-selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality where it can create meaningful user value. This is an applied leadership role: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel. What You Will Be Doing Define the evaluation strategy for conversational and agentic search, including offline and online evaluation, golden datasets, rubrics, LLM-as-judge calibration, groundedness and citation checks, and A/B testing. Lead the design of quality metrics and decision frameworks for RAG, agents, tools, model selection, agent routing, prompt behavior, and cost/latency trade-offs. Build, compare, and guide improvements across retrieval and re-ranking approaches, including sparse and dense retrieval, vector search, query understanding, semantic rewrites, and context enrichment. Turn experimental results into product and business decisions: which models to use, how to route requests efficiently, which tools should be exposed, and how agents should be customized for different Elastic use cases. Partner with engineering to productionize evaluation pipelines, telemetry, dashboards, CI guardrails, and regression detection for chat quality, helpfulness, dedication, latency, and cost. Influence the roadmap by identifying the highest-leverage quality gaps, proposing practical solutions, and communicating trade-offs clearly to product, engineering, and leadership. Mentor other data scientists and engineers in experiment design, evaluation methodology, statistical rigor, and practical approaches to improving LLM-powered systems. Share outcomes through clear docs, notebooks, PRs, dashboards, technical proposals, and cross-functional reviews. What You Bring 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences. Strong track record defining and leading evaluation for production AI/ML systems, including offline metrics, online experimentation, LLM-as-judge approaches, groundedness, citation quality, and model comparison. Experience influencing product and technical strategy through data, especially in ambiguous or emerging domains where the “right” metric or approach is not obvious at the start. Hands-on ability with Python, PyTorch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code. Strong understanding of retrieval systems, including dense and sparse retrieval, re-ranking, vector search, query understanding, and evaluation metrics such as nDCG, MRR, Recall@k, precision,and latency/cost trade-offs. Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking. Practical Elasticsearch experience, or experience with similar search and distributed data systems. ES|QL familiarity is a plus. Excellent written and verbal communication, with the ability to explain complex scientific and technical trade-offs to engineering, product, design, and leadership audiences. A collaborative, low-ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team. Compensation for this role is in the form of base salary. This role does not have a variable compensation component. At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets. The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position. The typical starting salary range for this role is: €73.300—€115.900 EUR Additional Information - We Take Care of Our People As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. Competitive pay based on the work you do here and not your previous salary Health coverage for you and your family in many locations Ability to craft your calendar with flexible locations and schedules for many roles Generous number of vacation days each year Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service Up to 40 hours each year to use toward volunteer projects you love Embracing parenthood with minimum of 16 weeks of parental leave Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation. We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission. Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster) Elasticsearch develops and distributes technology and information that is subject to U. S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic. Please see here for our Privacy Statement.", "summaryDescription" : "Principal Data Scientist - Agent Builder at Elastic", "postedDate" : "2026-06-15T23:26:19.410", "createdAt" : "2026-06-15T23:26:19.410", "lastUpdated" : "2026-06-16T08:10:42.353", "ingestTimestamp" : "2026-06-15T23:26:19.410", "mongoId" : "6a305a6af292fd229cf5054e", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "100k-150k", "hasSalary" : false, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 0.7777778, "_score" : 832.63947, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a305a6af292fd229cf5054a", "title" : "Principal Data Scientist - Agent Builder", "company" : "Elastic", "salary" : { "min" : 150000.0, "max" : 220000.0, "currency" : "EUR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market data for Principal Data Scientist in the Technology industry in the Netherlands, adjusted for executive seniority and full-time employment." }, "location" : "Netherlands", "locations" : [ "Netherlands" ], "timestamp" : "2026-06-16T07:36:20.025", "fingerprint" : "42b69b81ccf5dedf71320a5bc295c55ce32b7b0ec8e693c0f0fd92c726510c75", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://jobs.elastic.co/jobs?gh_jid=8008503&gh_jid=8008503", "externalId" : "8008503", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Netherlands", "continent" : "eu", "workMode" : "onsite" } ], "locationText" : "Netherlands", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "remoteFriendly" : true, "rawDescription" : "Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Search Conversational Experiences team builds Elastic’s new conversational and agentic platform that lets customers chat with their own data in Elasticsearch. We build the core quality layer for RAG, agents and tools, retrieval and citations, streaming, memory, and the evaluation signals that turn open-ended questions into grounded, reliable answers. As a Principal Data Scientist, you will help set the technical direction for how we evaluate, improve, and scale chat quality across Elastic’s agentic platform. You will define the evaluation strategy that guides product decisions, including which models we standardize on, how we route requests across agents, which tools we enable and when, and how we tailor agents to different Elastic use cases in search and beyond. You will work closely with backend engineering, product, UX, and other data scientists to turn ambiguous, cutting-edge problems into measurable product improvements. You’ll help lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to improve groundedness, shaping reasoning strategies and tool-selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality where it can create meaningful user value. This is an applied leadership role: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel. What You Will Be Doing Define the evaluation strategy for conversational and agentic search, including offline and online evaluation, golden datasets, rubrics, LLM-as-judge calibration, groundedness and citation checks, and A/B testing. Lead the design of quality metrics and decision frameworks for RAG, agents, tools, model selection, agent routing, prompt behavior, and cost/latency trade-offs. Build, compare, and guide improvements across retrieval and re-ranking approaches, including sparse and dense retrieval, vector search, query understanding, semantic rewrites, and context enrichment. Turn experimental results into product and business decisions: which models to use, how to route requests efficiently, which tools should be exposed, and how agents should be customized for different Elastic use cases. Partner with engineering to productionize evaluation pipelines, telemetry, dashboards, CI guardrails, and regression detection for chat quality, helpfulness, dedication, latency, and cost. Influence the roadmap by identifying the highest-leverage quality gaps, proposing practical solutions, and communicating trade-offs clearly to product, engineering, and leadership. Mentor other data scientists and engineers in experiment design, evaluation methodology, statistical rigor, and practical approaches to improving LLM-powered systems. Share outcomes through clear docs, notebooks, PRs, dashboards, technical proposals, and cross-functional reviews. What You Bring 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences. Strong track record defining and leading evaluation for production AI/ML systems, including offline metrics, online experimentation, LLM-as-judge approaches, groundedness, citation quality, and model comparison. Experience influencing product and technical strategy through data, especially in ambiguous or emerging domains where the “right” metric or approach is not obvious at the start. Hands-on ability with Python, PyTorch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code. Strong understanding of retrieval systems, including dense and sparse retrieval, re-ranking, vector search, query understanding, and evaluation metrics such as nDCG, MRR, Recall@k, precision,and latency/cost trade-offs. Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking. Practical Elasticsearch experience, or experience with similar search and distributed data systems. ES|QL familiarity is a plus. Excellent written and verbal communication, with the ability to explain complex scientific and technical trade-offs to engineering, product, design, and leadership audiences. A collaborative, low-ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team. Compensation for this role is in the form of base salary. This role does not have a variable compensation component. At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets. The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position. The typical starting salary range for this role is: €67.000—€106.000 EUR Additional Information - We Take Care of Our People As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. Competitive pay based on the work you do here and not your previous salary Health coverage for you and your family in many locations Ability to craft your calendar with flexible locations and schedules for many roles Generous number of vacation days each year Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service Up to 40 hours each year to use toward volunteer projects you love Embracing parenthood with minimum of 16 weeks of parental leave Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation. We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission. Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster) Elasticsearch develops and distributes technology and information that is subject to U. S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic. Please see here for our Privacy Statement.", "summaryDescription" : "Principal Data Scientist - Agent Builder at Elastic", "postedDate" : "2026-06-15T23:25:21.410", "createdAt" : "2026-06-15T23:25:21.410", "lastUpdated" : "2026-06-16T07:36:20.025", "ingestTimestamp" : "2026-06-15T23:25:21.410", "mongoId" : "6a305a6af292fd229cf5054a", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "150k-200k", "hasSalary" : false, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 0.7777778, "_score" : 832.63947, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a305a6af292fd229cf5054c", "title" : "Principal Data Scientist - Agent Builder", "company" : "Elastic", "industry" : "Technology", "salary" : { "min" : 150000.0, "max" : 220000.0, "currency" : "EUR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market data for a Principal Data Scientist in the Technology industry in the Netherlands, adjusted for executive seniority and full-time employment." }, "location" : "Netherlands", "locations" : [ "Netherlands" ], "timestamp" : "2026-06-16T07:17:32.597", "fingerprint" : "83fb6be16f038229467f7084b3e657227affb388d25a5b5e6bd0376be1f2d78f", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://jobs.elastic.co/jobs?gh_jid=8008504&gh_jid=8008504", "externalId" : "8008504", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Netherlands", "continent" : "eu", "workMode" : "onsite" } ], "locationText" : "Netherlands", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "remoteFriendly" : true, "rawDescription" : "Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Search Conversational Experiences team builds Elastic’s new conversational and agentic platform that lets customers chat with their own data in Elasticsearch. We build the core quality layer for RAG, agents and tools, retrieval and citations, streaming, memory, and the evaluation signals that turn open-ended questions into grounded, reliable answers. As a Principal Data Scientist, you will help set the technical direction for how we evaluate, improve, and scale chat quality across Elastic’s agentic platform. You will define the evaluation strategy that guides product decisions, including which models we standardize on, how we route requests across agents, which tools we enable and when, and how we tailor agents to different Elastic use cases in search and beyond. You will work closely with backend engineering, product, UX, and other data scientists to turn ambiguous, cutting-edge problems into measurable product improvements. You’ll help lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to improve groundedness, shaping reasoning strategies and tool-selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality where it can create meaningful user value. This is an applied leadership role: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel. What You Will Be Doing Define the evaluation strategy for conversational and agentic search, including offline and online evaluation, golden datasets, rubrics, LLM-as-judge calibration, groundedness and citation checks, and A/B testing. Lead the design of quality metrics and decision frameworks for RAG, agents, tools, model selection, agent routing, prompt behavior, and cost/latency trade-offs. Build, compare, and guide improvements across retrieval and re-ranking approaches, including sparse and dense retrieval, vector search, query understanding, semantic rewrites, and context enrichment. Turn experimental results into product and business decisions: which models to use, how to route requests efficiently, which tools should be exposed, and how agents should be customized for different Elastic use cases. Partner with engineering to productionize evaluation pipelines, telemetry, dashboards, CI guardrails, and regression detection for chat quality, helpfulness, dedication, latency, and cost. Influence the roadmap by identifying the highest-leverage quality gaps, proposing practical solutions, and communicating trade-offs clearly to product, engineering, and leadership. Mentor other data scientists and engineers in experiment design, evaluation methodology, statistical rigor, and practical approaches to improving LLM-powered systems. Share outcomes through clear docs, notebooks, PRs, dashboards, technical proposals, and cross-functional reviews. What You Bring 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences. Strong track record defining and leading evaluation for production AI/ML systems, including offline metrics, online experimentation, LLM-as-judge approaches, groundedness, citation quality, and model comparison. Experience influencing product and technical strategy through data, especially in ambiguous or emerging domains where the “right” metric or approach is not obvious at the start. Hands-on ability with Python, PyTorch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code. Strong understanding of retrieval systems, including dense and sparse retrieval, re-ranking, vector search, query understanding, and evaluation metrics such as nDCG, MRR, Recall@k, precision,and latency/cost trade-offs. Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking. Practical Elasticsearch experience, or experience with similar search and distributed data systems. ES|QL familiarity is a plus. Excellent written and verbal communication, with the ability to explain complex scientific and technical trade-offs to engineering, product, design, and leadership audiences. A collaborative, low-ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team. Compensation for this role is in the form of base salary. This role does not have a variable compensation component. At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets. The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position. The typical starting salary range for this role is: 831 700 kr—1 315 600 kr SEK Additional Information - We Take Care of Our People As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. Competitive pay based on the work you do here and not your previous salary Health coverage for you and your family in many locations Ability to craft your calendar with flexible locations and schedules for many roles Generous number of vacation days each year Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service Up to 40 hours each year to use toward volunteer projects you love Embracing parenthood with minimum of 16 weeks of parental leave Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation. We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission. Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster) Elasticsearch develops and distributes technology and information that is subject to U. S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic. Please see here for our Privacy Statement.", "summaryDescription" : "Principal Data Scientist - Agent Builder at Elastic", "postedDate" : "2026-06-15T23:24:36.526", "createdAt" : "2026-06-15T23:24:36.526", "lastUpdated" : "2026-06-16T07:17:32.597", "ingestTimestamp" : "2026-06-15T23:24:36.526", "mongoId" : "6a305a6af292fd229cf5054c", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "150k-200k", "hasSalary" : false, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 0.7777778, "_score" : 832.63947, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a305a6af292fd229cf5054b", "title" : "Principal Data Scientist - Agent Builder", "company" : "Elastic", "industry" : "Technology", "salary" : { "min" : 145000.0, "max" : 215000.0, "currency" : "EUR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market data for a Principal Data Scientist in the Technology industry in the Netherlands, adjusted for executive seniority and full-time employment." }, "location" : "Netherlands", "locations" : [ "Netherlands" ], "timestamp" : "2026-06-16T10:08:58.213", "fingerprint" : "6c178ddd639013f96ee2ba83d5834512f7afcf12c54c3c726385d95d5fe35e21", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://jobs.elastic.co/jobs?gh_jid=8008495&gh_jid=8008495", "externalId" : "8008495", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Netherlands", "continent" : "eu", "workMode" : "onsite" } ], "locationText" : "Netherlands", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "remoteFriendly" : true, "rawDescription" : "Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Search Conversational Experiences team builds Elastic’s new conversational and agentic platform that lets customers chat with their own data in Elasticsearch. We build the core quality layer for RAG, agents and tools, retrieval and citations, streaming, memory, and the evaluation signals that turn open-ended questions into grounded, reliable answers. As a Principal Data Scientist, you will help set the technical direction for how we evaluate, improve, and scale chat quality across Elastic’s agentic platform. You will define the evaluation strategy that guides product decisions, including which models we standardize on, how we route requests across agents, which tools we enable and when, and how we tailor agents to different Elastic use cases in search and beyond. You will work closely with backend engineering, product, UX, and other data scientists to turn ambiguous, cutting-edge problems into measurable product improvements. You’ll help lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to improve groundedness, shaping reasoning strategies and tool-selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality where it can create meaningful user value. This is an applied leadership role: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel. What You Will Be Doing Define the evaluation strategy for conversational and agentic search, including offline and online evaluation, golden datasets, rubrics, LLM-as-judge calibration, groundedness and citation checks, and A/B testing. Lead the design of quality metrics and decision frameworks for RAG, agents, tools, model selection, agent routing, prompt behavior, and cost/latency trade-offs. Build, compare, and guide improvements across retrieval and re-ranking approaches, including sparse and dense retrieval, vector search, query understanding, semantic rewrites, and context enrichment. Turn experimental results into product and business decisions: which models to use, how to route requests efficiently, which tools should be exposed, and how agents should be customized for different Elastic use cases. Partner with engineering to productionize evaluation pipelines, telemetry, dashboards, CI guardrails, and regression detection for chat quality, helpfulness, dedication, latency, and cost. Influence the roadmap by identifying the highest-leverage quality gaps, proposing practical solutions, and communicating trade-offs clearly to product, engineering, and leadership. Mentor other data scientists and engineers in experiment design, evaluation methodology, statistical rigor, and practical approaches to improving LLM-powered systems. Share outcomes through clear docs, notebooks, PRs, dashboards, technical proposals, and cross-functional reviews. What You Bring 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences. Strong track record defining and leading evaluation for production AI/ML systems, including offline metrics, online experimentation, LLM-as-judge approaches, groundedness, citation quality, and model comparison. Experience influencing product and technical strategy through data, especially in ambiguous or emerging domains where the “right” metric or approach is not obvious at the start. Hands-on ability with Python, PyTorch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code. Strong understanding of retrieval systems, including dense and sparse retrieval, re-ranking, vector search, query understanding, and evaluation metrics such as nDCG, MRR, Recall@k, precision,and latency/cost trade-offs. Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking. Practical Elasticsearch experience, or experience with similar search and distributed data systems. ES|QL familiarity is a plus. Excellent written and verbal communication, with the ability to explain complex scientific and technical trade-offs to engineering, product, design, and leadership audiences. A collaborative, low-ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team. Compensation for this role is in the form of base salary. This role does not have a variable compensation component. At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets. The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position. The typical starting salary range for this role is: €102.700—€162.500 EUR Additional Information - We Take Care of Our People As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. Competitive pay based on the work you do here and not your previous salary Health coverage for you and your family in many locations Ability to craft your calendar with flexible locations and schedules for many roles Generous number of vacation days each year Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service Up to 40 hours each year to use toward volunteer projects you love Embracing parenthood with minimum of 16 weeks of parental leave Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation. We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission. Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster) Elasticsearch develops and distributes technology and information that is subject to U. S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic. Please see here for our Privacy Statement.", "summaryDescription" : "Principal Data Scientist - Agent Builder at Elastic", "postedDate" : "2026-06-15T23:23:07.738", "createdAt" : "2026-06-15T23:23:07.738", "lastUpdated" : "2026-06-16T10:08:58.213", "ingestTimestamp" : "2026-06-15T23:23:07.738", "mongoId" : "6a305a6af292fd229cf5054b", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "100k-150k", "hasSalary" : false, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 0.7777778, "_score" : 832.63947, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2a60b674d8ee00a062058d", "title" : "Principal Scientist I, Antibody Engineering", "company" : "Sanofi", "industry" : "Biologics Research", "salary" : { "min" : 750000.0, "max" : 1200000.0, "currency" : "CNY", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Principal Scientist I, Antibody Engineering in Suzhou, China, adjusted for senior experience and the biotech industry." }, "location" : "China", "locations" : [ "China" ], "timestamp" : "2026-06-12T19:01:15.306", "fingerprint" : "162e16f8681a9e8e009272a763471c6abd168831127db24ed7e615fff6e357f2", "source" : "workday", "language" : "en", "applyUrl" : "https://sanofi.wd3.myworkdayjobs.com/SanofiCareers/job/Suzhou-Jiangsu/R-D---Intrepide---Principal-Scientist-I--Antibody-Engineering---SZ_R2859778-1", "externalId" : "R2859778", "industryFunction" : { }, "locationPoint" : "35.8617,104.1954", "structuredLocations" : [ { "city" : "Suzhou", "country" : "China", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "31.3111230,120.6212881" } ], "locationText" : "Suzhou, China", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Protein Engineering, Antibody Engineering, AI/ML, Computational Biology, Biochemistry, Biophysics, ProteinMPNN, RFdiffusion, ESMFold, AlphaFold2/3, Rosetta/PyRosetta, AbMPNN, Python", "professionalSkills" : [ "Structure-based design", "Generative AI", "Scientific excellence", "Platform innovation", "Program strategy", "Risk assessments", "Scientific curiosity", "Technical guidance" ], "hardSkillsList" : [ "Liability identification", "Affinity maturation", "CDR grafting", "Framework selection", "Back-mutation analysis", "Biophysical profiling", "Viscosity measurement", "Solubility assays", "Thermal stability", "Polyspecificity assays", "Phage display", "Yeast display", "Directed evolution", "Protein expression", "Protein purification", "Molecular biology techniques", "ML model interpretation", "Sequence design", "Structure design" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Liability identification", "level" : "intermediate" }, { "name" : "Affinity maturation", "level" : "intermediate" }, { "name" : "CDR grafting", "level" : "intermediate" }, { "name" : "Framework selection", "level" : "intermediate" }, { "name" : "Back-mutation analysis", "level" : "intermediate" }, { "name" : "Biophysical profiling", "level" : "intermediate" }, { "name" : "Viscosity measurement", "level" : "intermediate" }, { "name" : "Solubility assays", "level" : "intermediate" }, { "name" : "Thermal stability", "level" : "intermediate" }, { "name" : "Polyspecificity assays", "level" : "intermediate" }, { "name" : "Phage display", "level" : "intermediate" }, { "name" : "Yeast display", "level" : "intermediate" }, { "name" : "Directed evolution", "level" : "intermediate" }, { "name" : "Protein expression", "level" : "intermediate" }, { "name" : "Protein purification", "level" : "intermediate" }, { "name" : "Molecular biology techniques", "level" : "intermediate" }, { "name" : "ML model interpretation", "level" : "intermediate" }, { "name" : "Sequence design", "level" : "intermediate" }, { "name" : "Structure design", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Antibody Engineering, AI/ML Antibody Optimization, Protein Science, Developability Optimization", "majorSkillsList" : [ "Antibody Engineering", "AI/ML Antibody Optimization", "Protein Science", "Developability Optimization" ], "coreResponsibilities" : "Execute antibody optimization, Perform liability engineering, Identify deamidation hotspots, Identify oxidation hotspots, Identify isomerization hotspots, Identify aggregation hotspots, Lead affinity maturation campaigns, Execute antibody humanization strategies, Drive developability optimization, Address viscosity liabilities, Address solubility liabilities, Address thermal stability liabilities, Address polyspecificity liabilities, Serve as internal champion for AI-driven antibody optimization, Integrate computational design into engineering workflows, Coordinate with AI/ML CROs, Design computational optimization campaigns, Execute computational optimization campaigns, Define project scopes, Define deliverables, Define success criteria, Evaluate external AI/ML platforms, Select external AI/ML platforms, Translate computational sequence proposals, Design wet-lab validation experiments, Represent antibody engineering expertise, Provide scientific guidance, Shape program strategy, Accelerate candidate progression, Partner with discovery teams, Partner with structural biology teams, Partner with CMC teams, Partner with clinical teams, Align engineering strategies, Drive developability risk assessments, Contribute to IND-enabling activities, Champion new technologies, Foster scientific curiosity, Foster continuous improvement, Identify emerging technologies, Evaluate emerging technologies, Mentor junior scientists", "teamCollaboration" : "Multidisciplinary project teams; Cross-functional teams; External AI/ML CROs; Technology vendors; Discovery teams; Structural biology teams; CMC teams; Clinical teams", "domainKnowledge" : "Antibody Engineering; Protein Science; Biologics Research; Therapeutic Antibody Engineering; Computational Design; Structure-based Generative AI; Antibody Optimization Cycle; Protein Engineering; Immunology; Biophysics; Computational Biology; Deamidation; Oxidation; Isomerization; Aggregation; Affinity Maturation; Humanization; CDR Grafting; Framework Selection; Back-mutation Analysis; Developability Optimization; Viscosity; Solubility; Thermal Stability; Polyspecificity; Phage Display; Yeast Display; Directed Evolution; Biophysical Profiling; AI/ML CROs; IND-enabling activities", "domainList" : [ "antibody-engineering", "protein-science", "biologics-research", "therapeutic-antibody-engineering", "computational-design", "structure-based-generative-ai", "antibody-optimization-cycle", "protein-engineering", "immunology", "biophysics", "computational-biology", "deamidation", "oxidation", "isomerization", "aggregation", "affinity-maturation", "humanization", "cdr-grafting", "framework-selection", "back-mutation-analysis", "developability-optimization", "viscosity", "solubility", "thermal-stability", "polyspecificity", "phage-display", "yeast-display", "directed-evolution", "biophysical-profiling", "ai-ml-cros", "ind-enabling-activities" ], "domainSpecificKnowledge" : [ "Antibody Engineering", "Protein Science", "Biologics Research", "Therapeutic Antibody Engineering", "Computational Design", "Structure-based Generative AI", "Antibody Optimization Cycle", "Protein Engineering", "Immunology", "Biophysics", "Computational Biology", "Deamidation", "Oxidation", "Isomerization", "Aggregation", "Affinity Maturation", "Humanization", "CDR Grafting", "Framework Selection", "Back-mutation Analysis", "Developability Optimization", "Viscosity", "Solubility", "Thermal Stability", "Polyspecificity", "Phage Display", "Yeast Display", "Directed Evolution", "Biophysical Profiling", "AI/ML CROs", "IND-enabling activities" ], "toolProficiency" : [ "ProteinMPNN", "RFdiffusion", "ESMFold", "AlphaFold2/3", "Rosetta/PyRosetta", "AbMPNN" ], "communicationSkills" : "Translate scientific data; Clear insights; Actionable insights", "continuousLearning" : "Identify emerging technologies; Evaluate emerging technologies", "innovationCreativity" : "Platform innovation; Champion new technologies; Scientific curiosity; Continuous improvement; Emerging technologies", "problemSolvingSkills" : "Troubleshooting; Root cause analysis", "outcomesObjectives" : "Advance antibody engineering capabilities; Deliver next generation therapeutic molecules", "qualificationsBasic" : "5+ years antibody engineering experience, Ph.D. in Biochemistry, Protein Engineering, Immunology, Biophysics, Computational Biology, or related discipline, Hands-on experience in liability engineering, Demonstrated expertise in antibody humanization, Direct experience in biophysical characterization, Familiarity with antibody expression systems, Hands-on experience using ProteinMPNN, RFdiffusion, ESMFold, AlphaFold2/3, Rosetta/PyRosetta, AbMPNN, or similar, Practical experience applying ML models for antibody property prediction, Ability to independently run computational workflows, Working knowledge of Python or equivalent scripting", "qualificationsPreferred" : "Demonstrated track record of successfully advancing antibody candidates, Demonstrated expertise in antibody humanization experimental validation, Familiarity with generative AI approaches, Experience with fine-tuning or adapting pre-trained models, Mandarin proficiency is an advantage", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "**Job Title: Principal Scientist I, Antibody Engineering** Location: Suzhou, China **Position Summary** We are seeking an exceptional Principal Scientist, Antibody Engineering to join our Biologics Research team in Suzhou. This is a hands-on bench scientist role for a deep technical expert who thrives at the interface of classical antibody engineering and cutting-edge computational approaches. You will lead and execute the engineering of therapeutic antibody candidates — from liability removal and humanization through affinity maturation and developability optimization — while actively applying AI/ML-based tools to accelerate and enhance every stage of the antibody optimization cycle. This position is not primarily managerial. We are looking for a scientist who is equally comfortable overseeing protein science, interpreting developability findings, and deploying a structure-based generative AI model to design next-generation antibody variants. You will be a cornerstone of our antibody engineering capability, driving scientific excellence and platform innovation in a collaborative, fast-paced R&D environment. \\--- **Key Responsibilities** **1\\. Hands-On Antibody Engineering & Optimization** Execute the full spectrum of antibody optimization with direct bench involvement, from early liability identification through final candidate selection. Perform liability engineering to identify and resolve deamidation, oxidation, isomerization, and aggregation hotspots Lead affinity maturation campaigns using phage display, yeast display, or other directed evolution platforms Execute antibody humanization strategies including CDR grafting, framework selection, and back-mutation analysis Drive developability optimization through biophysical profiling — addressing viscosity, solubility, thermal stability, and polyspecificity liabilities **2\\. AI-Aided Protein Optimization & External Partner Collaboration** Serve as the internal champion for AI-driven antibody optimization, working closely with external AI/ML CROs and technology vendors to integrate computational design into engineering workflows. Coordinate with AI/ML CROs to design and execute computational optimization campaigns, defining project scopes, deliverables, and success criteria Evaluate and select appropriate external AI/ML platforms for specific engineering challenges Translate computational sequence proposals into structured wet-lab validation experiments, closing the loop between in silico design and in vitro outcomes **3\\. Cross-Functional Leadership & Program Delivery** Represent antibody engineering expertise within multidisciplinary project teams, providing scientific guidance that shapes program strategy and accelerates candidate progression. Partner with discovery, structural biology, CMC, and clinical teams to align engineering strategies with broader program objectives Drive developability risk assessments at key decision points and contribute to IND-enabling activities **4\\. Innovation & Mentorship** Champion new technologies and foster a culture of scientific curiosity and continuous improvement. Identify and evaluate emerging technologies in antibody engineering and computational design Mentor junior scientists through technical guidance, hands-on coaching, and career development support \\--- **Qualifications** **Educational Background & Experience** \\- Ph.D. in Biochemistry, Protein Engineering, Immunology, Biophysics, Computational Biology, or a closely related discipline is strongly preferred. \\- 5+ years of hands-on experience in antibody engineering within an industrial or advanced academic research setting. \\- Demonstrated track record of successfully advancing antibody candidates through engineering optimization cycles, with evidence of impact (publications, patents, program milestones). **Technical Expertise — Wet Lab (Hands-On)** \\- Proven hands-on experience in liability engineering: identification, prioritization, and experimental validation of deamidation, oxidation, aggregation, and isomerization hotspot removal. \\- Demonstrated expertise in antibody humanization: CDR grafting, framework selection, back-mutation analysis, and experimental validation of humanized variants is a plus. \\- Strong direct experience in biophysical characterization and developability profiling: DSF/nanoDSF, DLS, SEC, AC-SINS, HIC, viscosity measurement, and solubility/stability assays. \\- Familiarity with antibody expression systems (mammalian transient expression, yeast surface display), purification (Protein A/G, SEC), and standard molecular biology techniques. **AI/ML Competency — *Mandatory, Not Optional*** \\- Hands-on experience using one or more of the following (or equivalent tools): ProteinMPNN, RFdiffusion, ESMFold, AlphaFold2/3, Rosetta/PyRosetta, AbMPNN, or similar structure-based design platforms. \\- Practical experience applying ML models for antibody property prediction (e.g., aggregation, stability, affinity, immunogenicity scoring). \\- Ability to independently run computational workflows, interpret model outputs, and translate predictions into experimental hypotheses. \\- Familiarity with generative AI approaches for sequence and structure design; experience with fine-tuning or adapting pre-trained models on antibody-specific datasets is highly desirable. \\- Working knowledge of Python or equivalent scripting for data analysis and interfacing with computational tools is a strong advantage. **Collaboration & Communication Skills** \\- Strong ability to work effectively in cross-functional, matrixed environments. \\- Excellent written and verbal communication skills in English; Mandarin proficiency is an advantage in the Suzhou context. \\- Demonstrated ability to translate complex scientific data into clear, actionable insights for diverse audiences. \\--- We look forward to welcoming a Principal Scientist who will bring both deep bench expertise and computational boldness to advance our antibody engineering capabilities and deliver the next generation of therapeutic molecules for patients. # Pursue _progress_ , discover _extraordinary_ Better is out there. Better medications, better outcomes, better science. But progress doesn’t happen without people – people from different backgrounds, in different locations, doing different roles, all united by one thing: a desire to make miracles happen. So, let’s be those people. At Sanofi, we provide equal opportunities to all regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, ability or gender identity. Watch our [ALL IN video](https://www.youtube.com/watch?v=SkpDBZ-CJKw&t=67s) and check out our Diversity Equity and Inclusion actions at [sanofi.com](https://www.sanofi.com/en/our-responsibility/equality-and-inclusiveness)!", "summaryDescription" : "Principal Scientist I, Antibody Engineering at Sanofi. Skills: Antibody Engineering, AI/ML Antibody Optimization, Protein Science, Developability Optimization. Execute antibody optimization. Perform liability engineering", "postedDate" : "2026-06-10T14:26:01.590", "createdAt" : "2026-06-11T14:26:01.569", "lastUpdated" : "2026-06-12T19:01:15.306", "ingestTimestamp" : "2026-06-11T14:26:01.569", "mongoId" : "6a2a60b674d8ee00a062058d", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://sanofi.wd3.myworkdayjobs.com/SanofiCareers/job/Suzhou-Jiangsu/R-D---Intrepide---Principal-Scientist-I--Antibody-Engineering---SZ_R2859778-1", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Liability identification", "Affinity maturation", "CDR grafting", "Framework selection", "Back-mutation analysis", "Biophysical profiling", "Viscosity measurement", "Solubility assays", "Thermal stability", "Polyspecificity assays", "Phage display", "Yeast display", "Directed evolution", "Protein expression", "Protein purification", "Molecular biology techniques", "ML model interpretation", "Sequence design", "Structure design", "Leadership", "Mentoring", "Antibody Engineering", "AI/ML Antibody Optimization", "Protein Science", "Developability Optimization", "Structure-based design", "Generative AI", "Scientific excellence", "Platform innovation", "Program strategy", "Risk assessments", "Scientific curiosity", "Technical guidance", "Biologics Research", "Therapeutic Antibody Engineering", "Computational Design", "Structure-based Generative AI", "Antibody Optimization Cycle", "Protein Engineering", "Immunology", "Biophysics", "Computational Biology", "Deamidation", "Oxidation", "Isomerization", "Aggregation", "Affinity Maturation", "Humanization", "CDR Grafting", "Framework Selection", "Back-mutation Analysis", "Viscosity", "Solubility", "Thermal Stability", "Polyspecificity", "Phage Display", "Yeast Display", "Directed Evolution", "Biophysical Profiling", "AI/ML CROs", "IND-enabling activities", "ProteinMPNN", "RFdiffusion", "ESMFold", "AlphaFold2/3", "Rosetta/PyRosetta", "AbMPNN" ], "skillCount" : 67, "jobCategory" : "engineering", "seniorityLevel" : "senior", "dataQualityScore" : 1.0, "_score" : 814.2075, "daysOld" : 2, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Biologics Research" }, { "id" : "6a2a830a74d8ee00a06750e5", "title" : "Associate Principal / Principal Scientist (Protein Science)", "company" : "GenScript/ProBio", "industry" : "Biotechnology", "salary" : { "min" : 150000.0, "max" : 250000.0, "currency" : "SGD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market data for a Lead Senior Scientist in Biotech in Singapore, adjusted for company size and experience level." }, "location" : "Singapore", "locations" : [ "Singapore" ], "timestamp" : "2026-06-14T21:07:17.797", "fingerprint" : "54075c582deb9ed6c37fb7a1067f2adfb53aa8ddb1057b9dcb8ed7c4c8ba92e5", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/genscript/jobs/5160645007", "externalId" : "5160645007", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Singapore", "country" : "Singapore", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "1.2899175,103.8519072" } ], "locationText" : "Singapore, Singapore", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "technicalSkills" : "Protein science, Molecular engineering, Biologics development, Structure prediction, Computational tools", "professionalSkills" : [ "Functional leadership", "Platform building", "Client-facing projects", "Tech transfer", "Operational deployment", "Intellectual property" ], "hardSkillsList" : [ "Antibody design", "Protein expression", "Protein purification", "Bispecific design", "Protein engineering", "Biophysical characterization", "Protein-protein interactions", "Analytical techniques", "SPR", "Mass spectrometry" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Antibody design", "level" : "intermediate" }, { "name" : "Protein expression", "level" : "intermediate" }, { "name" : "Protein purification", "level" : "intermediate" }, { "name" : "Bispecific design", "level" : "intermediate" }, { "name" : "Protein engineering", "level" : "intermediate" }, { "name" : "Biophysical characterization", "level" : "intermediate" }, { "name" : "Protein-protein interactions", "level" : "intermediate" }, { "name" : "Analytical techniques", "level" : "intermediate" }, { "name" : "SPR", "level" : "intermediate" }, { "name" : "Mass spectrometry", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Bispecific Antibody Design, Protein Science, Molecular Engineering", "majorSkillsList" : [ "Bispecific Antibody Design", "Protein Science", "Molecular Engineering" ], "coreResponsibilities" : "Develop innovative strategies for design and engineering of, Implement innovative strategies for design and engineering of, Troubleshoot innovative strategies for design and engineering of, Design a portfolio of projects, Manage a portfolio of projects, Provide data-driven, structure-function insights, Improve expression, stability, and manufacturability, Oversee a team of research associates, Oversee a team of biotechnologists, Provide technical guidance to team, Foster a culture of excellence in protein science, Collaborate with cross-functional teams, Collaborate with cross-cultural teams, Support the development of biologics, Evaluate new technologies, Translate new technologies into production-ready solutions, Review advancements in antibody formats, Review advancements in engineering strategies, Review advancements in upstream and downstream integration impacts, Assess readiness of new technologies for tech transfer, Assess fit-for-purpose application across different molecule classes, Assess scalability and reproducibility, Assess impact on quality attributes, Act as a scientific bridge between R&D and, Ensure new platforms are scientifically sound, Ensure new platforms are translatable to production workflows, Ensure design decisions align with manufacturing realities, Partner with internal stakeholders, Develop design-for-manufacturability strategies, Develop internal guidelines for manufacturability, Develop AI-assisted design-for-manufacturability frameworks, Integrate AI-assisted design-for-manufacturability frameworks, Leverage internal datasets, Leverage external computational tools, Proactively identify risks, Proactively mitigate risks, Lead AI-enabled design initiatives, Support AI-enabled design initiatives, Translate computational design outputs into experimentally validated constructs, Ensure alignment between in silico design predictions and, Generate novel ideas, Contribute to intellectual property portfolio, Draft scientific papers, File patents, Report innovations in scientific forums, Contribute to development of scientific publications, Contribute to conference presentations, Contribute to webinars, Prepare technical reports, Review technical reports, Prepare technical documentation, Review technical documentation", "teamCollaboration" : "Cross-functional teams; Cross-cultural teams; Internal stakeholders; Quality Assurance; Process Development; Manufacturing", "projectManagementSkills" : [ "Project management" ], "domainKnowledge" : "Antibody formats; Protein science; Biologics production; Molecular engineering; Structure prediction; Developability scoring; Aggregation; Mispairing; Heterogeneity", "domainList" : [ "antibody-formats", "protein-science", "biologics-production", "molecular-engineering", "structure-prediction", "developability-scoring", "aggregation", "mispairing", "heterogeneity" ], "domainSpecificKnowledge" : [ "Antibody formats", "Protein science", "Biologics production", "Molecular engineering", "Structure prediction", "Developability scoring", "Aggregation", "Mispairing", "Heterogeneity" ], "toolProficiency" : [ "Computational tools" ], "communicationSkills" : "Scientific publications; Conference presentations; Webinars; Technical reports; Technical documentation", "documentationSkills" : "Technical documentation", "innovationCreativity" : "Innovation; Intellectual property; Novel ideas; R&D innovation", "problemSolvingSkills" : "Problem-solving skills; Troubleshooting", "qualificationsBasic" : "Ph.D. in Biochemistry, Molecular Biology, Cell Biology, Structural Biology, Bioengineering or related field, At least 5 years of experience in antibody design, protein expression, and purification, Expertise in bispecific design, Troubleshooting expression challenges, Demonstrated record of success with protein engineering for enhanced protein expression or properties, Experience with biophysical characterization of proteins, Experience with assessment of protein-protein interactions, Analytical and problem-solving skills, Wide ranging experience in analytical techniques, Excellent written and verbal English communication skills", "qualificationsPreferred" : "Experience in an industrial setting, Familiarity with automation and high-throughput techniques for protein expression and purification", "workSiteFlexibility" : "Onsite", "rawDescription" : "About GenScript GenScript Biotech Corporation (Stock Code: 1548.HK) is a global biotechnology group. Founded in 2002, GenScript has an established global presence across North America, Europe, the Greater China, and Asia Pacific. GenScript's businesses encompass four major categories based on its leading gene synthesis technology, including operation as a Life Science CRO, enzyme and synthetic biology products, biologics development and manufacturing, and cell therapy. GenScript is committed to striving towards its vision of being the most reliable biotech company in the world to make humans and nature healthier through biotechnology. Position Overview: We are seeking a highly motivated and experienced Principal Scientist in Bispecific Antibody Design & Engineering to establish and strengthen Singapore’s role as a center of excellence for complex antibody design and molecular engineering. In this role, you will serve as a functional leader in molecular design, format selection and recommendation, and engineering strategy while enabling improved expression, developability, and manufacturability. Additionally, you will play a key role in client-facing projects, ensuring seamless collaboration and delivery of high-quality biologics solutions. This position requires a blend of platform building, leadership skills, and the ability to build cross effectively. Key Responsibilities: Biologics Production Leadership: Develop, triage, implement, and troubleshoot innovative strategies for the design and engineering of complex antibody formats (bispecifics, multivalent, Fc-fusions, VHH). to achieve high yields, purity, and functionality. Design and manage a portfolio of projects to provide data-driven, structure-function insights to improve expression, stability, and manufacturability. Oversee a team of research associates and/or biotechnologists, providing technical guidance and fostering a culture of excellence in protein science methodologies. Cross-Functional Collaboration: Collaborate with cross-functional and cross-cultural teams to support the development of biologics, with a focus on evaluate and translate new technologies into production-ready solutions. Periodically review internal and external advancements in: Antibody formats and engineering strategies Upstream and downstream integration impacts driven by molecule design Assess readiness of new technologies for tech transfer, including: Fit-for-purpose application across different molecule classes Scalability and reproducibility Impact on quality attributes (aggregation, mispairing, heterogeneity) Act as a scientific bridge between R&D innovation and operational deployment, ensuring: New platforms are not only scientifically sound but also translatable to production workflows Design decisions are aligned with manufacturing realities Partner with internal stakeholders (e.g., Quality Assurance, Process Development, and Manufacturing) to ensure alignment and efficiency in biologics production workflows. Develop design-for-manufacturability strategies and internal guidelines: Develop and integrate AI-assisted design-for-manufacturability frameworks, leveraging internal datasets and external computational tools (e.g., structure prediction, developability scoring) to proactively identify and mitigate risks such as aggregation, mispairing, and low expression in complex antibody formats. Lead or support AI-enabled design and innovation initiatives (internal and external partnerships, including governmental or consortium projects) by translating computational design outputs into experimentally validated constructs, and ensuring alignment between in silico design predictions and real-world expression, quality, and manufacturability outcomes. Innovation & Intellectual Property: Generate novel ideas and contribute to the company’s intellectual property portfolio by drafting scientific papers, filing patents and reporting innovations in scientific forums. Contribute to the development of scientific publications, conference presentations, and webinars to showcase innovative findings and advancements. Prepare and review technical reports and documentation to internal and external stakeholders. Qualifications and Preferred Skills: Ph.D., in Biochemistry, Molecular Biology, Cell Biology, Structural Biology, Bioengineering or a related field. At least 5 years of experience in antibody design, protein expression, and purification, preferably in an industrial setting. Strong expertise in bispecific design and troubleshooting expression challenges. A demonstrated record of success with protein engineering for enhanced protein expression or properties. Experience with the biophysical characterization of proteins and the assessment of protein-protein interactions. Familiarity with automation and high-throughput techniques for protein expression and purification. Strong analytical and problem-solving skills, with a wide ranging experience in analytical techniques such as SPR, and mass spectrometry. Excellent written and verbal English communication skills, with a track record of scientific publications, presentations, and conferences. GenScript USA Inc/ProBio Inc. is a proud equal opportunity/affirmative action employer committed to attracting, retaining, and maximizing the performance of a diverse and inclusive workforce. It is the Company's policy to ensure equal employment opportunity without discrimination or harassment based on race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital or domestic/civil partnership status, genetic information, citizenship status, uniformed service member or veteran status, or any other characteristic protected by law. GenScript USA Inc./ProBio Inc. maintains a drug-free workplace. Please note: Genscript USA Inc./ProBio Inc. will only contact candidates through verified application sources. GenScript/Probio does not request personal information from candidates through individual email or any other platform.", "summaryDescription" : "Associate Principal / Principal Scientist (Protein Science) at GenScript/ProBio. Skills: Bispecific Antibody Design, Protein Science, Molecular Engineering. Develop innovative strategies for design and engineering of. Implement innovative strategies for design and engineering of", "postedDate" : "2026-06-11T22:09:12.938", "createdAt" : "2026-06-11T22:09:12.938", "lastUpdated" : "2026-06-14T21:07:17.797", "ingestTimestamp" : "2026-06-11T22:09:12.938", "mongoId" : "6a2a830a74d8ee00a06750e5", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "150k-200k", "hasSalary" : false, "allSkills" : [ "Antibody design", "Protein expression", "Protein purification", "Bispecific design", "Protein engineering", "Biophysical characterization", "Protein-protein interactions", "Analytical techniques", "SPR", "Mass spectrometry", "Leadership", "Mentoring", "Bispecific Antibody Design", "Protein Science", "Molecular Engineering", "Functional leadership", "Platform building", "Client-facing projects", "Tech transfer", "Operational deployment", "Intellectual property", "Project management", "Antibody formats", "Protein science", "Biologics production", "Molecular engineering", "Structure prediction", "Developability scoring", "Aggregation", "Mispairing", "Heterogeneity", "Computational tools" ], "skillCount" : 32, "jobCategory" : "other", "jobSubcategory" : "protein-&-antibody", "dataQualityScore" : 0.8888889, "_score" : 813.7322, "daysOld" : 2, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Biotechnology" }, { "id" : "6a2d58e574d8ee00a0b87933", "title" : "Scientist - Strain Engineering", "company" : "Cambrium", "industry" : "Biotech", "salary" : { "min" : 65000.0, "max" : 90000.0, "currency" : "EUR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Mid-level Scientist - Strain Engineering in Berlin, Germany, adjusted for the Biotech industry and contract employment type." }, "location" : "Berlin, DE", "locations" : [ "Berlin", "DE" ], "timestamp" : "2026-06-15T09:58:39.946", "fingerprint" : "a0b6573ce767a053620a2c031b15a2afa8b3567d6f25cb9582e2996e0459def8", "source" : "personio", "language" : "en", "applyUrl" : "https://cambrium.jobs.personio.de/job/2653146", "externalId" : "2653146", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Berlin", "country" : "Germany", "continent" : "eu", "source" : "llm", "workMode" : "onsite", "geo" : "52.5173885,13.3951309" } ], "locationText" : "Berlin, Germany", "employmentType" : "CONTRACT", "minimumEducation" : "Master's", "technicalSkills" : "Molecular biology, Yeast biology, Pichia, High-throughput screening, Lab automation, LIMS, Fermentations, Bioreactors, Computational biology, Machine learning, HPLC, MS", "professionalSkills" : [ "Strain engineering", "Project management", "Data analysis", "Decision-making", "Resource allocation" ], "hardSkillsList" : [ "Strain engineering", "Molecular biology", "Yeast biology", "High-throughput screening", "Lab automation", "Fermentations", "Bioreactors", "Computational biology", "Machine learning", "HPLC", "MS" ], "softSkillsList" : [ "Support", "Guide" ], "hardSkills" : [ { "name" : "Strain engineering", "level" : "intermediate" }, { "name" : "Molecular biology", "level" : "intermediate" }, { "name" : "Yeast biology", "level" : "intermediate" }, { "name" : "High-throughput screening", "level" : "intermediate" }, { "name" : "Lab automation", "level" : "intermediate" }, { "name" : "Fermentations", "level" : "intermediate" }, { "name" : "Bioreactors", "level" : "intermediate" }, { "name" : "Computational biology", "level" : "intermediate" }, { "name" : "Machine learning", "level" : "intermediate" }, { "name" : "HPLC", "level" : "intermediate" }, { "name" : "MS", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Support" }, { "name" : "Guide" } ], "majorSkills" : "Strain Engineering, Microbial Production, High-throughput Screening", "majorSkillsList" : [ "Strain Engineering", "Microbial Production", "High-throughput Screening" ], "coreResponsibilities" : "Manage strain engineering projects, Experimental design, Wet-lab execution, In silico data analysis, Run high-throughput screenings, Improve strain performance, Support up-scaling, Support bioprocess team, Run controlled fermentations, Support junior team members, Guide junior team members, Collaborate with computational engineers, Enhance platform tools, Manage large datasets, Ensure data integrity, Conduct data analysis, Drive informed decision-making, Present work, Communicate work, Align business goals, Align timelines", "teamCollaboration" : "Junior team members; Computational engineers; Bioprocess team; Internal stakeholders; External stakeholders", "projectManagementSkills" : [ "Project management", "Experimental design", "Resource allocation" ], "domainKnowledge" : "Molecular intelligence; Living systems; Enzymes; Polymers; Peptides; Recombinant molecules; Structural proteins; Bioprocess; Yeast biology; Pichia; Computational biology; Machine learning", "domainList" : [ "molecular-intelligence", "living-systems", "enzymes", "polymers", "peptides", "recombinant-molecules", "structural-proteins", "bioprocess", "yeast-biology", "pichia", "computational-biology", "machine-learning" ], "domainSpecificKnowledge" : [ "Molecular intelligence", "Living systems", "Enzymes", "Polymers", "Peptides", "Recombinant molecules", "Structural proteins", "Bioprocess", "Yeast biology", "Pichia", "Computational biology", "Machine learning" ], "toolProficiency" : [ "LIMS" ], "communicationSkills" : "Present work; Communicate work", "documentationSkills" : "Data integrity; Data analysis", "continuousLearning" : "Learning & Development Programme", "problemSolvingSkills" : "Data analysis; Decision-making", "specialRequirements" : "7-months fixed-term contract", "outcomesObjectives" : "Improve strain performance; Support up-scaling; Drive informed decision-making", "qualificationsBasic" : "PhD or MSc with 3+ years industry experience, Developing microbes for recombinant production, Molecular biology techniques, Yeast biology, High-throughput techniques, Lab automation, Proficient in LIMS", "qualificationsPreferred" : "Experience with controlled fermentations, Bioreactors, Computational biology methodologies, Machine learning methodologies, Analytical methods such as HPLC, MS", "workSiteFlexibility" : "Flexible", "rawDescription" : "What we do Cambrium is a molecular intelligence company engineering advanced materials that outperform. From personal care to textiles, mobility to home care, we design and manufacture materials that deliver real results today and for generations to come. Our platform brings together molecular intelligence and living systems to design and produce enzymes, polymers, and peptides at scale, unlocking performance characteristics that traditional chemistry could never access - specifically within polymers. We are an equal-opportunity employer and value diversity. We consider all applications equally regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply, and we can help with immigration. Your role We are looking for a Scientist - Strain Engineering, who will be responsible for building and screening microbial production strains and preparing them to be scaled. This position is a parental leave cover with a 7-months fixed-term contract starting October 2026. Your responsibilities: Manage strain engineering projects from experimental design and wet-lab execution to in silico data analysis. Run high-throughput screenings of strain libraries for the production of recombinant molecules, such as peptides, enzymes and structural proteins. Improve strain performance and support the up-scaling of commercial production strains. Support the bioprocess team by running controlled fermentations at lab-scale. Support and guide junior team members. Collaborate with computational engineers to enhance platform tools and manage large datasets from high-throughput screenings. Ensure data integrity across screening campaigns and conduct data analysis to drive informed decision-making. Present and communicate your work on a regular basis to internal and external stakeholders. Align business goals and timelines with strain engineering resource allocation. Your profile Must haves PhD, or MSc with 3+ years of industry experience, focused on developing microbes for recombinant production of proteins and enzymes. Strong experience with molecular biology techniques. Strong experience with yeast biology (experience with Pichia is a plus). Experience with high-throughput techniques and lab automation. Proficient in the use of LIMS (Laboratory Information Management System). Nice to haves Experience with controlled fermentations in bioreactors. Understanding of both computational biology and machine learning methodologies. Experience with analytical methods such as HPLC or MS. What we offer In addition to a competitive salary, you can expect the following: Flexible working hours Learning & Development Programme for all our team members Urban Sports Club Membership Subsidized lunch and impassioned lunch discussions Regular team events The opportunity to be part of our journey to a future of advanced materials that outperform", "summaryDescription" : "Scientist - Strain Engineering at Cambrium. Skills: Strain Engineering, Microbial Production, High-throughput Screening. Manage strain engineering projects. 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"Bellevue, Washington, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "Master's", "experienceMinYears" : 10, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, LLMs, Reinforcement learning, ML frameworks, AI systems, Multimodal pipelines, ML infrastructure, ML operations, Prompting, Retrieval-augmented generation, Chain-of-thought, Tool use, Long-term memory, Imitation learning", "professionalSkills" : [ "Technical strategy", "Productization", "System design", "ML infrastructure", "Model serving", "Continuous learning", "ML operations", "Agentic systems", "Research advances", "Automation workflows", "Agent behavior", "Cost analysis", "Latency analysis", "Quality analysis", "Technical leadership", "Experimentation", "Reproducibility", "Versioning", "Rigorous evaluation", "Ecosystem partners" ], "hardSkillsList" : [ "Agent-based automation", "Autonomous agents", "Multi-step reasoning", "LLM fine-tuning", "Computer-use modeling", "Agent orchestration", "Decision-making systems", "Model training", "Large-scale inference", "Model monitoring", "Drift detection", "Learning loops", "Offline evaluation", "Simulation environments", "Human-in-the-loop feedback", "A/B testing" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Agent-based automation", "level" : "intermediate" }, { "name" : "Autonomous agents", "level" : "intermediate" }, { "name" : "Multi-step reasoning", "level" : "intermediate" }, { "name" : "LLM fine-tuning", "level" : "intermediate" }, { "name" : "Computer-use modeling", "level" : "intermediate" }, { "name" : "Agent orchestration", "level" : "intermediate" }, { "name" : "Decision-making systems", "level" : "intermediate" }, { "name" : "Model training", "level" : "intermediate" }, { "name" : "Large-scale inference", "level" : "intermediate" }, { "name" : "Model monitoring", "level" : "intermediate" }, { "name" : "Drift detection", "level" : "intermediate" }, { "name" : "Learning loops", "level" : "intermediate" }, { "name" : "Offline evaluation", "level" : "intermediate" }, { "name" : "Simulation environments", "level" : "intermediate" }, { "name" : "Human-in-the-loop feedback", "level" : "intermediate" }, { "name" : "A/B testing", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Machine Learning, AI systems, Agent orchestration, LLM fine-tuning", "majorSkillsList" : [ "Machine Learning", "AI systems", "Agent orchestration", "LLM fine-tuning" ], "coreResponsibilities" : "Define and drive technical strategy for agent-based automation, Architect, prototype, and deploy advanced ML and AI, Lead the design and implementation of ML infrastructure, Partner closely with product, engineering, design, and go-to-market, Research state-of-the-art techniques, Apply techniques to automation workflows, Establish best practices, frameworks, and metrics for evaluating, Serve as a technical leader and mentor, Foster a culture of experimentation, reproducibility, versioning, and, Represent UiPath in the broader community", "teamCollaboration" : "Partner closely with product, engineering, design, and go-to-market teams; Collaboration with academia or ecosystem partners", "domainKnowledge" : "Agent-based automation; Autonomous agents; Enterprise automation; Foundation models; Agentic systems", "domainList" : [ "agent-based-automation", "autonomous-agents", "enterprise-automation", "foundation-models", "agentic-systems" ], "domainSpecificKnowledge" : [ "Agent-based automation", "Autonomous agents", "Enterprise automation", "Foundation models", "Agentic systems" ], "toolProficiency" : [ "ML frameworks", "ML libraries" ], "communicationSkills" : "Translate complex ML concepts", "innovationCreativity" : "Research advances; Experimentation", "problemSolvingSkills" : "Multi-step reasoning; Decision-making systems; Rigorous evaluation", "outcomesObjectives" : "Translate research advances into customer-facing capabilities; Drive high-impact outcomes", "qualificationsBasic" : "Advanced degree in Computer Science, Machine Learning, AI, or related field, or equivalent experience, 10+ years of industry experience in machine learning, Substantial experience building and operating ML systems in production, Deep expertise in modern ML frameworks and libraries, Proficiency in Python, Experience integrating ML models into large-scale software systems, Experience with large datasets, Experience with distributed training or inference, Experience with scalable architecture design, Experience with optimization for latency, throughput, and cost, Excellent communication skills", "qualificationsPreferred" : "MS or PhD preferred, Proven experience with large language models or foundation models, Fine-tuning, data preparation, inference optimization, or model evaluation experience", "workSiteFlexibility" : "Flexible", "rawDescription" : "LIFE AT UIPATH The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category-leading enterprise software that unleashes that power. To make that happen, we need people who are curious, self-propelled, generous, and genuine. People who love being part of a fast-moving, fast-thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose. Could that be you? As a Principal Applied Scientist, you will lead the architecture, research, and productization of these next-generation ML systems, bridging deep research with deployment at scale and shaping the future of enterprise automation. What You Will Do • Define and drive technical strategy for agent-based automation, including how autonomous agents use LLMs, reinforcement learning, simulation environments, tool use, and multi-step reasoning to integrate with the UiPath platform. • Architect, prototype, and deploy advanced ML and AI systems, covering LLM fine-tuning, multimodal pipelines, computer-use modeling, agent orchestration frameworks, and decision-making systems. • Lead the design and implementation of ML infrastructure and services for model training, fine-tuning, large-scale inference, model serving, monitoring, drift detection, continuous learning loops, and ML operations for agentic systems. • Partner closely with product, engineering, design, and go-to-market teams to translate research advances into customer-facing capabilities. • Research state-of-the-art techniques in prompting, retrieval-augmented generation, chain-of-thought, tool use, long-term memory, and RL or imitation learning for agent behavior, and apply them to automation workflows. • Establish best practices, frameworks, and metrics for evaluating agentic systems, including offline evaluation, simulation environments, human-in-the-loop feedback, A/B testing, and cost, latency, and quality analysis. • Serve as a technical leader and mentor across ML engineering, data science, and software engineering, fostering a culture of experimentation, reproducibility, versioning, and rigorous evaluation. • Represent UiPath in the broader community through publications, open-source contributions, conference participation, and collaboration with academia or ecosystem partners. What We Are Looking For • Advanced degree (MS or PhD preferred) in Computer Science, Machine Learning, AI, or a related field, or equivalent experience. • 10+ years of industry experience in machine learning, including substantial experience building and operating ML systems in production. • Deep expertise in modern ML frameworks and libraries, strong proficiency in Python, and experience integrating ML models into large-scale software systems. • Proven experience with large language models or foundation models, including fine-tuning, data preparation, inference optimization, or model evaluation. • Experience with large datasets, distributed training or inference, scalable architecture design, and optimization for latency, throughput, and cost. • Excellent communication skills with the ability to translate complex ML concepts to product and business audiences. • Demonstrated technical leadership in mentoring, influencing strategy, balancing research with delivery, and driving high-impact outcomes. #LI-MH1 Maybe you don’t tick all the boxes above—but still think you’d be great for the job? Go ahead, apply anyway. Please. Because we know that experience comes in all shapes and sizes—and passion can’t be learned. Many of our roles allow for flexibility in when and where work gets done. Depending on the needs of the business and the role, the number of hybrid, office-based, and remote workers will vary from team to team. Applications are assessed on a rolling basis and there is no fixed deadline for this requisition. The application window may change depending on the volume of applications received or may close immediately if a qualified candidate is selected. We value a range of diverse backgrounds, experiences and ideas. We pride ourselves on our diversity and inclusive workplace that provides equal opportunities to all persons regardless of age, race, color, religion, sex, sexual orientation, gender identity, and expression, national origin, disability, neurodiversity, military and/or veteran status, or any other protected classes. Additionally, UiPath provides reasonable accommodations for candidates on request and respects applicants' privacy rights. To review these and other legal disclosures, visit our privacy policy https://www.uipath.com/legal/trust-and-security/privacy-policy.", "summaryDescription" : "Principal Applied Scientist at UiPath. Skills: Machine Learning, AI systems, Agent orchestration, LLM fine-tuning. Define and drive technical strategy for agent-based automation. Architect, prototype, and deploy advanced ML and AI", "postedDate" : "2026-06-11T16:30:27.984", "createdAt" : "2026-06-11T16:30:27.984", "lastUpdated" : "2026-06-13T20:12:20.454", "ingestTimestamp" : "2026-06-11T16:30:27.984", "mongoId" : "6a2a7e5b74d8ee00a0668caa", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 200000, "salaryAnnualMax" : 250000, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "Agent-based automation", "Autonomous agents", "Multi-step reasoning", "LLM fine-tuning", "Computer-use modeling", "Agent orchestration", "Decision-making systems", "Model training", "Large-scale inference", "Model monitoring", "Drift detection", "Learning loops", "Offline evaluation", "Simulation environments", "Human-in-the-loop feedback", "A/B testing", "Leadership", "Mentoring", "Machine Learning", "AI systems", "Technical strategy", "Productization", "System design", "ML infrastructure", "Model serving", "Continuous learning", "ML 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"timestamp" : "2026-06-15T09:19:51.035", "fingerprint" : "2b6cf8770992355deefd6060592c16532820a8715d4cb5d23b3970eb2b103232", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3102574/apply", "externalId" : "a33fb0c2-2341-4604-a786-849c6a5de168", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Boston", "state" : "Massachusetts", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "42.3588336,-71.0578303" } ], "locationText" : "Boston, Massachusetts, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, Java, C++, Generative AI, Large language models, Multimodal models, Computer vision, Machine learning, PyTorch, TensorFlow, Scikit-learn, Hugging Face", "professionalSkills" : [ "Algorithm design", "Model structure", "Scientific reviews", "Experimental design", "Problem decomposition", "Strategy development" ], "hardSkillsList" : [ "Predictive modeling", "Data analysis", "Algorithm design", "Model structure", "Scientific reviews", "Experimental design", "Problem decomposition", "Strategy development" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Predictive modeling", "level" : "intermediate" }, { "name" : "Data analysis", "level" : "intermediate" }, { "name" : "Algorithm design", "level" : "intermediate" }, { "name" : "Model structure", "level" : "intermediate" }, { "name" : "Scientific reviews", "level" : "intermediate" }, { "name" : "Experimental design", "level" : "intermediate" }, { "name" : "Problem decomposition", "level" : "intermediate" }, { "name" : "Strategy development", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Generative AI, Machine Learning, Large language models, Computer Vision", "majorSkillsList" : [ "Generative AI", "Machine Learning", "Large language models", "Computer Vision" ], "coreResponsibilities" : "Define research directions, Invent machine learning techniques, Conduct rigorous experiments, Publish research results, Translate research into practice, Develop long-term strategies, Persuade teams to adopt strategies, Propose goals, Deliver on goals, Participate in organizational planning, Participate in hiring, Participate in mentorship, Participate in leadership development, Build scalable science solutions, Build scalable engineering solutions, Serve as scientific resource, Lead scientific reviews, Review experimental design, Review modeling, Review verification, Review research procedures, Probe assumptions, Illuminate pitfalls, Foster shared understanding, Align teams toward strategies, Educate scientific community, Keep community updated, Guide career growth, Develop junior scientists, Develop junior leads, Drive development of GenAI, Handle Amazon-scale use cases, Impact customer experiences, Develop multimodal LLMs, Develop Gen AI, Provide customer experience", "teamCollaboration" : "Cross-organization views; Organizational planning; Leadership development", "domainKnowledge" : "Artificial General Intelligence; Generative AI; Large language models; Multimodal models; Computer vision; Amazon-scale use cases", "domainList" : [ "artificial-general-intelligence", "generative-ai", "large-language-models", "multimodal-models", "computer-vision", "amazon-scale-use-cases" ], "domainSpecificKnowledge" : [ "Artificial General Intelligence", "Generative AI", "Large language models", "Multimodal models", "Computer vision", "Amazon-scale use cases" ], "toolProficiency" : [ "PyTorch", "TensorFlow", "Scikit-learn", "Hugging Face" ], "innovationCreativity" : "Invent machine learning techniques; Develop long-term strategies", "problemSolvingSkills" : "Decompose complex problems; Tackle intrinsically hard problems", "outcomesObjectives" : "Impact customer experiences", "qualificationsBasic" : "5+ years predictive modeling, PhD in Electrical Engineering, PhD in Computer Science, PhD in Mathematics, Experience programming in Java, Experience programming in C++, Experience programming in Python, Leading experienced scientists, Developing junior members", "qualificationsPreferred" : "10+ years of experience, Experience with AWS, Experience with GCP, Experience with Azure, Experience with PyTorch, Experience with TensorFlow, Experience with scikit-learn, Experience with Hugging Face, Experience with large language models, Experience with multimodal models, Experience with computer vision, Experience with Generative AI, Experience with Amazon-scale use cases, Experience with scientific reviews, Experience with experimental design, Experience with modeling, Experience with verification, Experience with research procedures, Experience with mentoring, Experience with hiring, Experience with developing scientists, Experience with developing leads, Experience with organizational planning, Experience with leadership development, Experience with full-cycle development, Experience with conception, Experience with design, Experience with implementation, Experience with testing, Experience with documentation, Experience with delivery, Experience with maintenance, Experience with multimodal LLMs, Experience with Gen AI, Experience with Computer Vision", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "As a Principal Scientist within the Artificial General Intelligence (AGI) organization, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modeling, verification and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. You will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team The AGI team has a mission to push the envelope with multimodal LLMs and Gen AI in Computer Vision, in order to provide the best-possible experience for our customers. Basic Qualifications: - 5+ years of hands-on work in predictive modeling and analysis experience - PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field - Experience working in predictive modeling and analysis - Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives - Experience programming in Java, C++, Python or related language - Experience with leading experienced scientists as well as having a record of developing junior members from academia or industry to a career track in a business environment Preferred Qualifications: - 10+ years of relevant work in industry or academia experience - Knowledge of problem solving, algorithm design and complexity analysis - Experience creating novel algorithms and advancing the state of the art - Have peer-reviewed scientific contributions in premier journals and conferences Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.", "summaryDescription" : "Principal Applied Scientist, AGI Foundations at Amazon.com Services LLC. Skills: Generative AI, Machine Learning, Large language models, Computer Vision. Define research directions. Invent machine learning techniques", "postedDate" : "2026-06-14T03:15:36.099", "createdAt" : "2026-06-14T03:15:36.099", "lastUpdated" : "2026-06-15T09:19:51.035", "ingestTimestamp" : "2026-06-14T03:15:36.099", "mongoId" : "68e6b1dc70f0b99d58d66e3a", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3102574/apply", "salaryAnnualMin" : 179000, "salaryAnnualMax" : 309400, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Predictive modeling", "Data analysis", "Algorithm design", "Model structure", "Scientific reviews", "Experimental design", "Problem decomposition", "Strategy development", "Leadership", "Mentoring", "Generative AI", "Machine Learning", "Large language models", "Computer Vision", "Artificial General Intelligence", "Multimodal models", "Computer vision", "Amazon-scale use cases", "PyTorch", "TensorFlow", "Scikit-learn", "Hugging Face" ], "skillCount" : 22, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 1.0, "_score" : 799.8698, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2cba5a74d8ee00a09f906e", "title" : "Senior or Principal Data Scientist/Machine Learning Scientist", "industry" : "Technology", "salary" : { "min" : 175000.0, "max" : 250000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Senior Data Scientist/Machine Learning Scientist in Cambridge, United States, adjusted for seniority and industry." }, "location" : "Cambridge, MA", "locations" : [ "Cambridge, MA" ], "certifications" : [ "GCP Professional Data Engineer", "AWS Data Analytics", "Databricks Certified", "Dbt Certified" ], "timestamp" : "2026-06-15T05:15:21.554", "fingerprint" : "f157367f29879db9d85a91c4e1327e0df66eb8c53d3924a931697cb5c8582155", "source" : "jazzhr", "language" : "en", "externalId" : "tljr8NqfPM", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Cambridge", "state" : "Massachusetts", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "42.3656347,-71.1040018" } ], "locationText" : "Cambridge, Massachusetts, United States", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, R, SQL, Spark, Snowflake, BigQuery, Redshift, Databricks, Dbt, Fivetran, Airflow, AWS, SageMaker, Athena, GCP, Vertex AI, Azure, Synapse, ML Studio, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Hugging Face, MLflow", "professionalSkills" : [ "Data engineering", "ML/AI", "Business intelligence", "Analytics engineering", "Quantitative analysis" ], "hardSkillsList" : [ "Statistical methods", "Modelling skills", "Regression", "Classification", "Clustering", "Time-series analysis", "A/B testing", "Causal inference" ], "hardSkills" : [ { "name" : "Statistical methods", "level" : "intermediate" }, { "name" : "Modelling skills", "level" : "intermediate" }, { "name" : "Regression", "level" : "intermediate" }, { "name" : "Classification", "level" : "intermediate" }, { "name" : "Clustering", "level" : "intermediate" }, { "name" : "Time-series analysis", "level" : "intermediate" }, { "name" : "A/B testing", "level" : "intermediate" }, { "name" : "Causal inference", "level" : "intermediate" } ], "majorSkills" : "Data science, Machine learning", "majorSkillsList" : [ "Data science", "Machine learning" ], "coreResponsibilities" : "Develop and deploy machine learning models, Design and implement data pipelines, Build and maintain BI dashboards, Perform statistical analysis, Conduct quantitative research, Collaborate with engineering teams, Communicate findings to stakeholders", "teamCollaboration" : "Cross-functional teams; Stakeholder communication", "domainKnowledge" : "Cloud data stacks", "domainList" : [ "cloud-data-stacks" ], "domainSpecificKnowledge" : [ "Cloud data stacks" ], "toolProficiency" : [ "Tableau", "Power BI", "Looker" ], "communicationSkills" : "Present findings", "qualificationsBasic" : "5+ years experience, Master's degree in Statistics, SQL proficiency", "qualificationsPreferred" : "PhD preferred, GCP Professional Data Engineer certification, AWS Data Analytics certification, Databricks Certified certification, Dbt Certified certification", "workSiteFlexibility" : "Onsite", "summaryDescription" : "Senior or Principal Data Scientist/Machine Learning Scientist. 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Partner with engineering leaders; Work with talented scientists; Work with talented peers", "projectManagementSkills" : [ "Roadmap creation" ], "domainKnowledge" : "Amazon Payments AI; Recommendation systems; Prediction systems; GEN AI platform; SOP automation", "domainList" : [ "amazon-payments-ai", "recommendation-systems", "prediction-systems", "gen-ai-platform", "sop-automation" ], "domainSpecificKnowledge" : [ "Amazon Payments AI", "Recommendation systems", "Prediction systems", "GEN AI platform", "SOP automation" ], "communicationSkills" : "Written communication; Verbal communication", "innovationCreativity" : "Expects and requires innovation", "problemSolvingSkills" : "Dive deep", "qualificationsBasic" : "PhD or MSc in computer science, 10+ years in AI and Machine Learning, Track record of thought leadership, skills in collaboration and teamwork, record of delivery in industry and academic contexts, Ability to handle multiple competing priorities, Excellent written and verbal communication skills", "qualificationsPreferred" : "12+ years in AI, Machine Learning, and Natural Language Processing, publication history in top publications", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "We are looking for a Principal Applied Scientist for Amazon Payments AI/ML Team, which contributes science and science-related engineering work for Amazon’s Payments Artificial Intelligence services (e.g. Amazon Payments Recommendation, Prediction, GEN AI platform to enable SOP automation and new projects already underway). In this role, you will work with your peers and senior management to set the direction for Amazon’s AI efforts. Our mission is to put the power of AI in the hands of every developer. You will be responsible for mentoring a team of applied scientists. You will be responsible for creating a strong environment for applied scientists, with a focus on recruiting, retaining and developing top talent. You will partner with engineering leaders to deliver remarkable new Amazon Payments services and features that leverage Machine Learning and GenAI. As a Principal Applied Scientist, you will identify research directions, create roadmaps for forward-looking research and communicate them to senior leadership, and work closely with engineering teams to bring research to production. You will work with teams of talented scientists, and fill the ranks by attracting the best scientists in machine learning, e.g. Amazon payments recommendation and natural language processing for SOP automation. You will work with talented peers and leverage Amazon’s heterogeneous data sources and large-scale computing resources. Basic Qualifications: - PhD or MSc in computer science or related fields - 10 + years in AI and Machine Learning in industry and/or academia - Track record of thought leadership and contributions that have advanced the field - Strong skills in collaboration and teamwork - Strong record of delivery in industry and academic contexts - Ability to handle multiple competing priorities in a fast-paced environment. - Excellent written and verbal communication skills Preferred Qualifications: - 12+ years in AI, Machine Learning, and Natural Language Processing in industry and/or academia. - Strong publication history in top publications. Professional traits that are not unique to this position, but necessary for Amazon leaders: - Exhibits excellent judgment - Hires and develops great people - Has relentlessly high standards (is never satisfied with the status quo) - Is able to dive deep and is never out of touch with the details of the business or the technology - Expects and requires innovation of her/his team - Has the innate ability to inspire passion in others - Strong results orientation - Thinks big Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, WA, Seattle - 198,900.00 - 269,000.00 USD annually", "summaryDescription" : "Principal Applied Scientist, Amazon Payments at Amazon.com Services LLC. Skills: AI, Machine Learning, GenAI. Mentor a team of applied scientists. 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"Upstream Process Development", "Technology Transfer", "GMP Manufacturing Support" ], "coreResponsibilities" : "Provide technical oversight, Provide strategic input, Lead platform improvement projects, Manage platform improvement projects, Coordinate platform improvement projects, Support late-stage development, Identify development tasks, Plan development activities, Monitor deliverable progress, Generate protocols, Generate reports, Qualify scale-down models, Characterize processes, Validate scale-down models, Support technology transfer activities, Perform experiments, Implement process improvements, Characterize processes at scale, Provide MSAT technical support, Provide manufacturing technical support, Troubleshoot processes, Analyze manufacturing data, Perform root cause analysis, Manage deviations during GMP production, Manage change controls, Manage CAPAs, Manage CCRs, Design experimental plans, Analyze experimental results, Review experimental results, Optimize scale-down models, Maintain scale-down models, Write lab notebook entries, Approve lab notebook entries, Write experimental protocols, Approve experimental protocols, Write development reports, Approve development reports, Write process descriptions, Approve process descriptions, Write scientific reports, Approve scientific reports, Present project progress, Present project results, Maintain communication with functions, Supervise USP lab activities, Guide junior team members, Set goals for team members, Assign tasks to team members", "teamCollaboration" : "Cross-functional teams; External partners; Analytical Development; Manufacturing; Engineering; MSAT; Relevant functions", "projectManagementSkills" : [ "Program Strategy" ], "domainKnowledge" : "Gene therapy; Viral vectors; Cell therapy; GMP manufacturing; EMA/FDA regulations; ICH guidelines", "domainList" : [ "gene-therapy", "viral-vectors", "cell-therapy", "gmp-manufacturing", "ema-fda-regulations", "ich-guidelines" ], "domainSpecificKnowledge" : [ "Gene therapy", "Viral vectors", "Cell therapy", "GMP manufacturing", "EMA/FDA regulations", "ICH guidelines" ], "toolProficiency" : [ "Electronic Lab Notebooks" ], "communicationSkills" : "External presentations; Technical discussion; Internal presentations; Client presentations", "documentationSkills" : "Protocol generation; Report generation; Lab notebook entries; Process descriptions; Scientific reports; Quality Systems documentation", "innovationCreativity" : "Innovative problem solvers", "problemSolvingSkills" : "Problem solvers; Process troubleshooting; Root cause analysis", "specialRequirements" : "Work schedule may change, Criminal background check required, Sit for long periods, Lift up to 10 pounds, Lift up to 25 pounds", "qualificationsBasic" : "MSc with 8+ years of relevant work experience, PhD with 6+ years of relevant work experience, Process Development and Cell Line Development Experience, Upstream Processing Experience with Cell Culture, Bioreactor Technologies, and Transfection/Infection approaches, Hands-on experience on complex biologics, Experience with technology transfer and GMP manufacturing technical support, Experience with EMA/FDA regulations, ICH", "qualificationsPreferred" : "Prior work experience at a CDMO", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "The field of gene therapy is growing rapidly and Genezen is here to meet the exacting needs of our clients. We provide process development, produce GMP viral vectors, and provide cell transduction for gene and cell therapy clinical trials and are proud to be a part of the process that leads to new cures. WORKING AT GENEZEN Many people have worked in environments where you feel like just another nameless part of an enormous machine. Genezen is different. We want our employees to be innovative, collaborative problem solvers who are dedicated to meeting the challenges of working at a fast-growing company. We are dedicated to empowering our employees to be their best and making Genezen a rewarding place to work. Every employee contributes to the success of the company and you can be part of that team. JOB SUMMARY The Sr./Principal Scientist, Upstream Process Development will be part of the Process Development Group reporting to the Director of Process Development. The role will have direct management responsibility for a team of Scientists and Research Associates. The Sr/Principal Scientist is responsible for the Upstream Process development, characterization, technology transfer and GMP manufacturing technical support. This individual will lead or contribute as a core member of various cross-functional teams, and interact extensively with other departments within Genezen, including Analytical Development, Manufacturing, Engineering, and MSAT. This is a client-facing position with regular interaction with our partners including external presentations, driving the technical discussion and program strategy. JOB RESPONSIBILITIES Provides technical oversight and strategic input for upstream viral vector drug substance development activities Leads, proposes, manages, and coordinates activities on platform improvement/continuous projects in USP labs Leads, manages, and supports late-stage development, by identifying tasks, planning, monitoring progress of deliverables, and in terms of protocol and report generation in areas such as scale-down model qualification, process characterization, and scale-down model process validation studies Leads and supports technology transfer activities at pilot plant and when necessary, preforms experiments him/herself, focusing scale-up platform process at scale and process improvements implementation, process characterization at scale and bills of materials as well as sampling plans. Provides MSAT and manufacturing technical Upstream SME support for process troubleshooting, manufacturing data analysis, root cause analysis, and deviations during GMP production, change controls, CAPAs, CCRs and other Quality Systems documentation as needed. Designs and outlines experimental plans to meet the defined Genezen and client objective. Analyses and reviews experimental results and data. Performs, optimizes, and maintains linear scale-down models. Writes, reviews, and approves electronic lab notebooks (ELNs) entries, experimental protocols, development reports, process descriptions, and scientific reports Presents project progress and results internally to cross-functional teams and externally to clients/stakeholders Maintains robust communication with all relevant functions that have an impact on or are impacted by Upstream Process Development Coordinates and supervises the activities in the USP labs (with a team of engineers/ (Jr.) bioprocess technologist, co-op students, interns, temporary personnel (contractors), and technical SMEs and when necessary, performs experiments him/herself. Guides junior USP team members by setting goals/tasks on projects SPECIAL JOB REQUIREMENTS Adaptability required as work schedule may change based on business needs Criminal background check required Other duties as assigned KNOWLEDGE, SKILLS AND EXPERIENCE EDUCATION / CERTIFICATIONS / LICENSES Essential/Desired MSc with 8+ years of relevant work experience in Bioprocess Engineering/ Life Science Biotechnology/Biochemistry/ Protein Chemistry, Chemical Engineering or related disciplines Essential PhD with 6+ years of relevant work experience in Bioprocess Engineering/ Life Science Biotechnology/Biochemistry/ Protein Chemistry, Chemical Engineering or related disciplines Desired ON-THE-JOB EXPERIENCE Process Development and Cell Line Development Experience Essential Upstream Processing Experience with Cell Culture, Bioreactor Technologies, and Transfection/Infection approaches Essential Prior work experience at a CDMO Desired SKILLS / ABILITIES Hands-on experience on complex biologics, e. g. gene therapy, viral vaccines, therapeutic proteins Essential Experience with technology transfer and GMP manufacturing technical support Essential Experience with EMA/FDA regulations, ICH guidelines, GMP and quality systems Essential Effective communicator with track record of presenting data to relevant stakeholders including external stakeholders Essential Experience with DoE design and statistical analysis on large data sets Essential Excellent scientific writing skills demonstrated by publications and reports Essential Experience with direct management of Scientists and Research Associates including day-to-day oversight and direction and career development and coaching. Essential PHYSICAL DEMANDS While performing the duties of this job, the employee is required to meet the following physical demands: Work Environment Regularly sit for long periods of time Movement Frequently required to stand; sit; use hands to finger, handle, or feel; reach with hands and arms Occasionally required to walk; climb or balance; and stoop, kneel, crouch, or crawl Lifting Frequently lift and/or move up to 10 pounds Rarely lift and/or move up to 25 pounds Vision Frequently utilize close vision and the ability to adjust focus Communication Frequently required to communicate by talking, hearing, using telephone and e-mail GENEZEN'S CURES VALUE-BASED COMPETENCIES Committed to Science We are committed to scientific excellence, staying current with industry developments, making data-driven decisions, and pursuing innovation to advance healthcare. Urgency in action for the patients We operate with urgency and a commitment to delivering timely treatments to patients by accelerating our partners' programs. Resilience however, we are not able to sponsor visas. Genezen has operates two locations: one in Fishers, Indiana and the second in Lexington, Massachusetts. Fishers is a suburb of Indianapolis. Fishers was named the #1 place to live in the US by Money Magazine in 2017 for its livability, safety and entrepreneurship. Fishers is one of the fastest growing communities in Indiana and is dedicated to supporting a high quality of life for all its residents. Nearby Indianapolis is home to professional and college sports teams, the Indy 500, a growing art community and the world’s largest children’s museum--to name just a few reasons why this area is a great place to live. Lexington, Massachusetts, located just northwest of Boston, is renowned as a historic suburb with a rich cultural heritage. It boasts picturesque New England charm, tree-lined streets, and a vibrant community. Known for its pivotal role in the American Revolution, Lexington offers visitors and residents alike a blend of historical landmarks, modern amenities, and a strong sense of community pride.", "summaryDescription" : "Sr/Principal Scientist - Upstream Process Development at Genezen. Skills: Upstream Process Development, Technology Transfer, GMP Manufacturing Support. Provide technical oversight. Provide strategic input", "postedDate" : "2026-06-16T01:20:25.644", "createdAt" : "2026-06-16T01:20:25.644", "lastUpdated" : "2026-06-16T12:09:40.636", "ingestTimestamp" : "2026-06-16T01:20:25.644", "mongoId" : "6a307f69f292fd229c058f0d", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 165000, "salaryAnnualMax" : 235000, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Process Development", "Cell Line Development", "Upstream Processing", "Cell Culture", "Bioreactor Technologies", "Transfection", "Infection approaches", "Technology Transfer", "GMP Manufacturing", "Scale-up", "Process Improvements", "Process Characterization", "Data Analysis", "Root Cause Analysis", "Quality Systems", "Experimental Design", "Scale-down Models", "Adaptability", "Upstream Process Development", "GMP Manufacturing Support", "Bills of Materials", "Sampling Plans", "Process Troubleshooting", "Change Controls", "CAPAs", "CCRs", "Data Review", "Lab Notebooks", "Protocols", "Development Reports", "Process Descriptions", "Scientific Reports", "Client Presentations", "Program Strategy", "Gene therapy", "Viral vectors", "Cell therapy", "GMP manufacturing", "EMA/FDA regulations", "ICH guidelines", "Electronic Lab Notebooks" ], "skillCount" : 41, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Lead", "dataQualityScore" : 1.0, "_score" : 768.50305, "daysOld" : 0, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Biotech", "llmIndustryConfidence" : 0.95 }, { "id" : "6a30872bf292fd229c08682a", "title" : "Principal Scientist, Lentivirus Downstream Process Development", "company" : "Legend Biotech", "industry" : "Biotech", "salary" : { "min" : 146410.0, "max" : 192164.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Somerset, New Jersey, United States", "locations" : [ "Somerset", "New Jersey", "United States" ], "timestamp" : "2026-06-16T10:10:10.746", "fingerprint" : "30ce75b581742f4b4c5865aeb50ecd1d9c003e26c177b21bea469c2472692342", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/legendcareers/jobs/4706012005", "externalId" : "4706012005", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Somerset", "state" : "New Jersey", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "40.5675084,-74.6189556" } ], "locationText" : "Somerset, New Jersey, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "PhD", "experienceMinYears" : 8, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Lentivirus, Viral vectors, Cell therapies, TCR-T, NK cell, Biotechnology, Chemical Engineering, Biomedical Engineering, Virology, Molecular Biology, Purification, Chromatography, TFF, GMP, CMC, IND, IMPD, Engineering modeling, Optimization", "professionalSkills" : [ "Process development", "Manufacturing", "Project management", "Tech transfer", "Regulatory compliance", "Scientific rigor", "Continuous improvement", "Strategic focus", "Troubleshooting" ], "hardSkillsList" : [ "Vector recovery", "Vector purity", "Vector potency" ], "softSkillsList" : [ "Leadership", "Emotional intelligence" ], "hardSkills" : [ { "name" : "Vector recovery", "level" : "intermediate" }, { "name" : "Vector purity", "level" : "intermediate" }, { "name" : "Vector potency", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Emotional intelligence" } ], "majorSkills" : "Viral Vector Purification, Process Development, GMP Manufacturing", "majorSkillsList" : [ "Viral Vector Purification", "Process Development", "GMP Manufacturing" ], "coreResponsibilities" : "Implement state-of-the-art knowledge management for DSP data, Communicate potential issues and delays, Communicate solutions and mitigation approaches, Identify project acceleration opportunities, Leverage advanced science and technology, Maximize vector recovery, purity, and potency, Identify creative solutions to define unknowns, Guide a team of scientists, Foster a culture of transparency, Foster a culture of scientific rigor, Foster a culture of continuous improvement, Author technical documents, Review technical documents, Work closely with Upstream Process Development, Work closely with Analytical Development, Ensure integrated process outcomes, Ensure holistic process outcomes, Tech transfer process, Resolve conflict, Proactively identify performance issues, Address performance issues, Ensure compliance with regulatory requirements", "teamCollaboration" : "Senior Leaders; CMC Project Manager; Project Leader; Technical leads; Upstream Process Development; Analytical Development", "projectManagementSkills" : [ "Project management" ], "domainKnowledge" : "Viral vector field; GMP manufacturing requirements; CMC strategies; Early-phase clinical trials", "domainList" : [ "viral-vector-field", "gmp-manufacturing-requirements", "cmc-strategies", "early-phase-clinical-trials" ], "domainSpecificKnowledge" : [ "Viral vector field", "GMP manufacturing requirements", "CMC strategies", "Early-phase clinical trials" ], "communicationSkills" : "Technical documentation; Regulatory submissions", "documentationSkills" : "Development reports; SOPs; CMC sections", "innovationCreativity" : "Creative solutions", "problemSolvingSkills" : "Troubleshooting skills", "outcomesObjectives" : "Maximize vector recovery; Maximize vector purity; Maximize vector potency", "qualificationsBasic" : "PhD or MS degree, 8+ years (PhD) experience, 10+ years (MS) experience, Deep technical hands-on experience in purification, Deep technical hands-on experience in chromatography, Deep technical hands-on experience in TFF, Understanding of GMP manufacturing, Understanding of CMC strategies, Deep knowledge in cell technologies, Deep knowledge in lentiviral vector technologies, Ability to implement closed-system processes, Ability to maximize cost effectiveness, Knowledge in engineering modeling, Knowledge in optimization, Excellent leadership, Excellent emotional intelligence, Excellent communication", "qualificationsPreferred" : "PhD preferred, Developing vector processes for CAR-T, Developing vector processes for systemic delivery, Handling large scale (>50L) purification process, Mandarin language is a plus", "remoteFriendly" : true, "workSiteFlexibility" : "Hybrid", "rawDescription" : "Legend Biotech is a global biotechnology company dedicated to treating, and one day curing, life-threatening diseases. Headquartered in Somerset, New Jersey, we are developing advanced cell therapies across a diverse array of technology platforms, including autologous and allogenic chimeric antigen receptor T-cell, T-cell receptor (TCR-T), and natural killer (NK) cell-based immunotherapy. From our three R implement state-of-the-art knowledge management for DSP data. Communicate the potential for issues and delays along with solutions and mitigation approaches. In collaboration with the Senior Leaders, CMC Project Manager, Project Leader, and other technical leads identify project acceleration opportunities. Leverage advanced science and technology to maximize vector recovery, purity, and potency, identifying creative solutions to define unknowns in the viral vector field. Guide a team of scientists in the execution of project-specific activities, fostering a culture of transparency, scientific rigor, and continuous improvement. Author and serve as a primary reviewer for technical documents, including development reports, SOPs, and CMC sections for regulatory submissions (IND/IMPD). Work closely with Upstream Process Development and Analytical Development teams to ensure integrated and holistic process outcomes. Tech transfer process to/from other Legend sites or CDMOs Resolve conflict and proactively identify/address performance issues. Ensure compliance with regulatory requirements. Requirements Advanced degree (PhD preferred, or MS) in Chemical Engineering, Biomedical Engineering, Virology, Molecular Biology, or a related field. 8+ years (PhD) or 10+ years (MS) of experience in process development or manufacturing within a biotech or pharmaceutical setting. Deep technical hands-on experience in purification, chromatography, and TFF specifically for viral vectors (Lentivirus preferred) Strong understanding of GMP manufacturing requirements and CMC strategies for early-phase clinical trials. Deep knowledge and hands-on experiences in cell and lentiviral vector technologies within the current emerging field Ability to implement closed-system processes and maximize cost effectiveness Strong knowledge in engineering modeling and optimization. Excellent leadership, emotional intelligence, and communication skills; ability to maintain composure and strategic focus during challenging development timelines. Creative, strategic thinking Strong troubleshooting skills Preferred experience in developing vector processes tailored for CAR-T generation and systemic delivery Preferred experience in handling large scale(>50L) purification process. Language: English. Mandarin is a plus. #Li-JR1 #Li-Hybrid The base pay range below is what Legend Biotech USA Inc. reasonably expects to offer at the time of posting. Actual compensation may vary based on experience, skills, qualifications, and geographic location. The company reserves the right to modify this range as needed and in accordance with applicable laws. Other Types of Pay: Performance-based bonus and/or equity is available to employees in eligible roles. Benefits and Paid Time Off: Medical, dental, and vision insurance as well as a 401(k) retirement plan with a company match that vests fully on day one. We offer eight (8) weeks of paid parental leave after just three (3) months of employment, and a paid time off policy that includes vacation time, personal time, sick time, floating holidays, and eleven (11) company holidays. Additional voluntary benefits include flexible spending and health savings accounts, life and AD demonstrating our ongoing commitment to building a culture where our people feel empowered, supported, and inspired to do their best work. Pay Range (Base Pay): $146,410—$192,164 USD Please note: These benefits are offered exclusively to permanent full-time employees. Contractors are not eligible for benefits through Legend Biotech. EEO Statement It is the policy of Legend Biotech to provide equal employment opportunities without regard to actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression (including transgender status), sexual orientation, marital status, military service and veteran status, disability, genetic information, or any other protected characteristic under applicable federal, state or local laws or ordinances. Employment is at-will and may be terminated at any time with or without cause or notice by the employee or the company. For information related to our privacy notice, please review: Legend Biotech Privacy Notice.", "summaryDescription" : "Principal Scientist, Lentivirus Downstream Process Development at Legend Biotech. Skills: Viral Vector Purification, Process Development, GMP Manufacturing. Implement state-of-the-art knowledge management for DSP data. Communicate potential issues and delays", "postedDate" : "2026-06-16T01:22:16.605", "createdAt" : "2026-06-16T01:22:16.605", "lastUpdated" : "2026-06-16T10:10:10.746", "ingestTimestamp" : "2026-06-16T01:22:16.605", "mongoId" : "6a30872bf292fd229c08682a", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 146410, "salaryAnnualMax" : 192164, "salaryBand" : "100k-150k", "hasSalary" : true, "allSkills" : [ "Vector recovery", "Vector purity", "Vector potency", "Leadership", "Emotional intelligence", "Viral Vector Purification", "Process Development", "GMP Manufacturing", "Process development", "Manufacturing", "Project management", "Tech transfer", "Regulatory compliance", "Scientific rigor", "Continuous improvement", "Strategic focus", "Troubleshooting", "Viral vector field", "GMP manufacturing requirements", "CMC strategies", "Early-phase clinical trials" ], "skillCount" : 21, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 1.0, "_score" : 759.44446, "daysOld" : 0, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Biotech", "llmIndustryConfidence" : 0.98 }, { "id" : "6a30872bf292fd229c08682b", "title" : "Principal Scientist, Upstream Process, Process Development", "company" : "Legend Biotech", "industry" : "Biotech", "salary" : { "min" : 146410.0, "max" : 192164.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Somerset, New Jersey, United States", "locations" : [ "Somerset", "New Jersey", "United States" ], "timestamp" : "2026-06-16T10:46:06.092", "fingerprint" : "6a9a03ed4722086d519f0ca78f36667e4eac56fc0784b1643134bbc33c4a68c5", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/legendcareers/jobs/4706014005", "externalId" : "4706014005", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Somerset", "state" : "New Jersey", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "40.5675084,-74.6189556" } ], "locationText" : "Somerset, New Jersey, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "MS", "experienceMinYears" : 8, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Chemical Engineering, Biomedical Engineering, Virology, Molecular Biology, Mammalian cell culture, HEK293, HEK293T, Seed train expansion, Feeding strategy, Transient transfection, Viral vectors, Lentivirus, Bioreactor, Engineering modeling, Mass transfer, Bioprocess optimization", "professionalSkills" : [ "Process development", "Manufacturing", "Scale-up models", "Scale-down models", "CMC strategies", "Tech transfer" ], "hardSkillsList" : [ "Cell culture", "Transfection optimization", "Bioreactor operation", "Process optimization", "Regulatory compliance" ], "softSkillsList" : [ "Leadership", "Emotional intelligence", "Strategic thinking", "Troubleshooting" ], "hardSkills" : [ { "name" : "Cell culture", "level" : "intermediate" }, { "name" : "Transfection optimization", "level" : "intermediate" }, { "name" : "Bioreactor operation", "level" : "intermediate" }, { "name" : "Process optimization", "level" : "intermediate" }, { "name" : "Regulatory compliance", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Emotional intelligence" }, { "name" : "Strategic thinking" }, { "name" : "Troubleshooting" } ], "majorSkills" : "Upstream Process Development, Viral Vector Production, Bioreactor Scale-up, Process Optimization", "majorSkillsList" : [ "Upstream Process Development", "Viral Vector Production", "Bioreactor Scale-up", "Process Optimization" ], "coreResponsibilities" : "Implement state-of-the-art knowledge management, Communicate potential issues and delays, Communicate solutions and mitigation approaches, Identify project acceleration opportunities, Leverage science and technology, Maximize vector titer, Maximize specific productivity, Maximize quality, Identify creative solutions, Define unknowns in viral vector production, Guide scientists in project execution, Foster culture of transparency, Foster culture of scientific rigor, Foster culture of continuous improvement, Author technical documents, Review technical documents, Work closely with Downstream Process Development, Work closely with Analytical Development, Ensure integrated process outcomes, Ensure holistic process outcomes, Perform tech transfer, Resolve conflict, Proactively identify performance issues, Address performance issues, Ensure compliance with regulatory requirements", "teamCollaboration" : "Senior Leaders; CMC Project Manager; Project Leader; Technical leads; Downstream Process Development; Analytical Development", "projectManagementSkills" : [ "Project acceleration" ], "domainKnowledge" : "Biotechnology; Cell therapies; Viral vector production; Mammalian cell culture; Lentivirus; GMP manufacturing; CMC strategies; Early-phase clinical trials; Engineering modeling; Mass transfer; Bioprocess optimization", "domainList" : [ "biotechnology", "cell-therapies", "viral-vector-production", "mammalian-cell-culture", "lentivirus", "gmp-manufacturing", "cmc-strategies", "early-phase-clinical-trials", "engineering-modeling", "mass-transfer", "bioprocess-optimization" ], "domainSpecificKnowledge" : [ "Biotechnology", "Cell therapies", "Viral vector production", "Mammalian cell culture", "Lentivirus", "GMP manufacturing", "CMC strategies", "Early-phase clinical trials", "Engineering modeling", "Mass transfer", "Bioprocess optimization" ], "toolProficiency" : [ "Bioreactors" ], "communicationSkills" : "Technical documentation; Regulatory submissions", "documentationSkills" : "Development reports; SOPs; CMC sections", "innovationCreativity" : "Creative solutions", "problemSolvingSkills" : "Troubleshooting skills", "outcomesObjectives" : "Maximize vector titer; Maximize specific productivity; Maximize quality", "qualificationsBasic" : "MS or PhD, 8+ years (PhD) experience, 10+ years (MS) experience, Mammalian cell culture experience, Seed train expansion experience, Feeding strategy experience, Transient transfection optimization experience, Viral vector experience, Lentivirus experience, Bioreactor scale-up models experience, Bioreactor scale-down models experience, GMP manufacturing requirements knowledge, CMC strategies knowledge, Cell technology knowledge, Lentiviral vector technology knowledge, Engineering modeling knowledge, Mass transfer knowledge, Bioprocess optimization knowledge", "qualificationsPreferred" : "PhD preferred, Large-scale bioreactor processes experience, Mandarin language a plus", "remoteFriendly" : true, "workSiteFlexibility" : "Onsite", "rawDescription" : "Legend Biotech is a global biotechnology company dedicated to treating, and one day curing, life-threatening diseases. Headquartered in Somerset, New Jersey, we are developing advanced cell therapies across a diverse array of technology platforms, including autologous and allogenic chimeric antigen receptor T-cell, T-cell receptor (TCR-T), and natural killer (NK) cell-based immunotherapy. From our three R implement state-of-the-art knowledge management for USP data. Communicate the potential for issues and delays along with solutions and mitigation approaches. In collaboration with the Senior Leaders, CMC Project Manager, Project Leader, and other technical leads, identify project acceleration opportunities. Leverage advanced science and technology to maximize vector titer, specific productivity, and quality, identifying creative solutions to define unknowns in the viral vector production field. Guide a team of scientists in the execution of project-specific activities, fostering a culture of transparency, scientific rigor, and continuous improvement. Author and serve as a primary reviewer for technical documents, including development reports, SOPs, and CMC sections for regulatory submissions (IND). Work closely with Downstream Process Development and Analytical Development teams to ensure integrated and holistic process outcomes. Tech transfer process to/from other Legend sites or CDMOs. Resolve conflict and proactively identify/address performance issues. Ensure compliance with regulatory requirements. Requirements Advanced degree (PhD preferred, or MS) in Chemical Engineering, Biomedical Engineering, Virology, Molecular Biology, or a related field. 8+ years (PhD) or 10+ years (MS) of experience in process development or manufacturing within a biotech or pharmaceutical setting Deep technical hands-on experience in mammalian cell culture (HEK293, HEK293T), seed train expansion, feeding strategy and transient transfection optimization specifically for viral vectors (Lentivirus preferred). Proven expertise in developing and implementing bioreactor scale-up and scale-down models. Strong understanding of GMP manufacturing requirements and CMC strategies for early-phase clinical trials. Deep knowledge and hands-on experiences in cell and lentiviral vector technologies within the current emerging field Strong knowledge in engineering modeling, mass transfer, and bioprocess optimization. Excellent leadership, emotional intelligence, and communication skills; ability to maintain composure and strategic focus during challenging development timelines. Creative, strategic thinking. Strong troubleshooting skills Preferred experience in handling large-scale (>50L, e.g., 200L/500L) upstream bioreactor processes. Language(s): English. Mandarin is a plus. #Li-JR1 #Li-Onsite The base pay range below is what Legend Biotech USA Inc. reasonably expects to offer at the time of posting. Actual compensation may vary based on experience, skills, qualifications, and geographic location. The company reserves the right to modify this range as needed and in accordance with applicable laws. Other Types of Pay: Performance-based bonus and/or equity is available to employees in eligible roles. Benefits and Paid Time Off: Medical, dental, and vision insurance as well as a 401(k) retirement plan with a company match that vests fully on day one. We offer eight (8) weeks of paid parental leave after just three (3) months of employment, and a paid time off policy that includes vacation time, personal time, sick time, floating holidays, and eleven (11) company holidays. Additional voluntary benefits include flexible spending and health savings accounts, life and AD demonstrating our ongoing commitment to building a culture where our people feel empowered, supported, and inspired to do their best work. Pay Range (Base Pay): $146,410—$192,164 USD Please note: These benefits are offered exclusively to permanent full-time employees. Contractors are not eligible for benefits through Legend Biotech. EEO Statement It is the policy of Legend Biotech to provide equal employment opportunities without regard to actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression (including transgender status), sexual orientation, marital status, military service and veteran status, disability, genetic information, or any other protected characteristic under applicable federal, state or local laws or ordinances. Employment is at-will and may be terminated at any time with or without cause or notice by the employee or the company. For information related to our privacy notice, please review: Legend Biotech Privacy Notice.", "summaryDescription" : "Principal Scientist, Upstream Process, Process Development at Legend Biotech. Skills: Upstream Process Development, Viral Vector Production, Bioreactor Scale-up, Process Optimization. Implement state-of-the-art knowledge management. Communicate potential issues and delays", "postedDate" : "2026-06-16T01:21:58.038", "createdAt" : "2026-06-16T01:21:58.038", "lastUpdated" : "2026-06-16T10:46:06.092", "ingestTimestamp" : "2026-06-16T01:21:58.038", "mongoId" : "6a30872bf292fd229c08682b", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 146410, "salaryAnnualMax" : 192164, "salaryBand" : "100k-150k", "hasSalary" : true, "allSkills" : [ "Cell culture", "Transfection optimization", "Bioreactor operation", "Process optimization", "Regulatory compliance", "Leadership", "Emotional intelligence", "Strategic thinking", "Troubleshooting", "Upstream Process Development", "Viral Vector Production", "Bioreactor Scale-up", "Process Optimization", "Process development", "Manufacturing", "Scale-up models", "Scale-down models", "CMC strategies", "Tech transfer", "Project acceleration", "Biotechnology", "Cell therapies", "Viral vector production", "Mammalian cell culture", "Lentivirus", "GMP 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"https://pfizer.wd1.myworkdayjobs.com/PfizerCareers/job/United-States---Washington--Bothell/Principal-Scientist--Process-Chemistry_4958939-1", "externalId" : "4958939", "industryFunction" : { }, "locationPoint" : "51.43667,5.97917", "structuredLocations" : [ { "city" : "Bothell", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.7623204,-122.2054035" } ], "locationText" : "Bothell, Washington, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Organic chemistry, Process development, Analytical methods, Synthetic routes, High-throughput experimentation, Data-rich experimentation, Catalysis, Continuous processing, Predictive technologies, Computational tools, Predictive software", "professionalSkills" : [ "Route screening", "Process development", "Process characterization", "Scale-up", "GMP manufacturing", "Technology transfer", "Resource management" ], "hardSkillsList" : [ "Synthetic organic chemistry", "Process development", "Engineering", "Analytical technologies", "Purification technologies", "GMP deliveries", "Regulatory authoring" ], "softSkillsList" : [ "Technical leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Synthetic organic chemistry", "level" : "intermediate" }, { "name" : "Process development", "level" : "intermediate" }, { "name" : "Engineering", "level" : "intermediate" }, { "name" : "Analytical technologies", "level" : "intermediate" }, { "name" : "Purification technologies", "level" : "intermediate" }, { "name" : "GMP deliveries", "level" : "intermediate" }, { "name" : "Regulatory authoring", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Technical leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Process Chemistry, Drug-linker development, ADC therapeutics", "majorSkillsList" : [ "Process Chemistry", "Drug-linker development", "ADC therapeutics" ], "coreResponsibilities" : "Provide technical leadership, Drive development of safe chemistry, Leverage state-of-the-art technologies, Spearhead advancement of oncology portfolio, Oversee external resources, Manage technology transfer, Manage campaign execution, Serve as technical leader, Serve as strategic leader, Mentor junior staff, Propose technical solutions, Propose strategic solutions, Represent process chemistry, Design laboratory experiments, Execute laboratory experiments, Analyze complex data, Maintain laboratory records, Maintain reports, Maintain patents, Maintain regulatory documents, Maintain external communications, Employ state-of-the-art technologies, Be recognized as technical leader, Author publications, Present externally, Drive technical aspects, Lead technology transfer, Manage CRO/CMO resources, Communicate abstract concepts, Communicate ideas, Communicate solutions, Establish timelines, Deliver on timelines, Promote supportive environment, Promote inclusive environment, Maintain understanding of activities, Maintain understanding of research, Lead authoring for patents, Lead authoring for regulatory documents, Guide cross-functional team decisions, Build relationships with stakeholders, Advocate for department", "teamCollaboration" : "Cross-functional teams", "domainKnowledge" : "Antibody-drug conjugates; Drug-linkers; Targeted cancer therapy; Pharmaceutical development; Regulated pharmaceutical environment", "domainList" : [ "antibody-drug-conjugates", "drug-linkers", "targeted-cancer-therapy", "pharmaceutical-development", "regulated-pharmaceutical-environment" ], "domainSpecificKnowledge" : [ "Antibody-drug conjugates", "Drug-linkers", "Targeted cancer therapy", "Pharmaceutical development", "Regulated pharmaceutical environment" ], "communicationSkills" : "Oral communication; Written communication; Persuasive communication", "documentationSkills" : "Patent authoring; Regulatory document authoring", "continuousLearning" : "Presenting at scientific meetings", "innovationCreativity" : "Scientific innovation", "specialRequirements" : "Laboratory-based position, Use of PPE, Occasional travel, Permanent work authorization in the United States", "outcomesObjectives" : "Deliver breakthroughs; Accelerate delivery of medicines; Develop robust processes; Drive next generation therapy; Achieve portfolio goals", "qualificationsBasic" : "PhD in Organic Chemistry with 4+ years of experience in pharmaceutical process development, Master's degree with 9+ years of relevant experience, Bachelors degree and 12+ years of experience, Proven track record of scientific innovation, Sound understanding of modern synthetic organic chemistry principles, In-depth knowledge of pharmaceutical development, Experience in a regulated pharmaceutical environment, GMP deliveries and regulatory authoring experience", "qualificationsPreferred" : "Experience with drug-linkers for ADCs, Experience mentoring scientific staff, Experience with technology transfer activities, CRO/CDMO resource management experience", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "**OVERVIEW:** Pfizer’s purpose is to deliver breakthroughs that change patients’ lives. Process Research and Development is at the heart of fulfilling Pfizer’s purpose as we work to translate advanced science and technologies into the therapies that matter most. Whether you are in the discovery sciences, ensuring drug safety and efficacy or supporting clinical trials, you will apply cutting edge design and process development capabilities to accelerate and bring the best-in-class medicines to patients around the world. **ROLE SUMMARY:** The Chemical Process and Analytical Development (CPAD) group located in Bothell, WA is responsible for the development of chemical processes and analytical methods for novel drug-linkers (linker-payloads) used in antibody-drug conjugates (ADCs). Our process chemists, analytical chemists, and chemical engineers collaborate to discover phase-appropriate synthetic routes and chemical processes to accelerate the delivery of life saving medicines to patients, and to develop robust and innovative synthetic processes to novel commercial drug-linker candidates that drive the next generation of targeted cancer therapy. As a Principal Scientist in CPAD, you will provide technical leadership to a team of process chemists engaged in route screening/selection, process development, and process characterization for our growing drug-linker portfolio. You will drive the development of safe and scalable chemistry by leveraging state-of-the-art technologies such as high-throughput experimentation, data-rich experimentation, catalysis, continuous processing, and predictive technologies. You will spearhead the advancement of Pfizer’s oncology portfolio through personal laboratory effort as well as oversee external resources through technology transfer and campaign execution at CDMOs. You will serve as a technical and strategic leader for early _and_ late-phase programs while mentoring junior staff members in a supportive and inclusive work environment. You will propose technical and strategic solutions to management and represent process chemistry in cross-functional teams requiring excellent communication skills. Finally, you will be expected to establish an external presence as technical leader in the field by authoring journal articles and presenting at leading external scientific meetings. **ROLE RESPONSIBILITIES:** * Design and execute well-planned laboratory experiments that require complex data analysis. * Maintain high standards for all laboratory records, reports, patents, regulatory documents, and external communications. * Employ state-of-the art technologies including high-throughput experimentation, data-rich experimentation, computational tools, and predictive software to accelerate process development. * Be recognized as a technical leader internally and externally by authoring publications and presenting externally. * Drive technical aspects of early _and_ late phase pharmaceutical development. * Lead technology transfer to internal sites or external CDMOs for GMP manufacturing while managing CRO/CMO resources. * Persuasively communicate abstract concepts, ideas, and solutions to project teams, leadership, and cross-functional teams. * Establish and deliver on tight timelines aligned with our portfolio goals. * Promote a supportive and inclusive work environment and mentor junior staff. * Maintain an understanding of the activities and research carried out by the project team members. * Lead authoring for patents and regulatory documents. * Leverage knowledge and experience to guide cross-functional team decisions. * Build strong relationships with stakeholders to advocate for our department. **BASIC QUALIFICATIONS:** * PhD in Organic Chemistry with 4+ years of experience in pharmaceutical process development, including route design, scale-up, and GMP manufacturing; or a Master’s degree with 9+ years of relevant experience OR Bachelors degree and 12+ years of experience. * Proven track record of scientific innovation and impact, demonstrated through authorship of peer-reviewed publications, patents, or presentations at recognized scientific conferences. * A sound understanding of modern synthetic organic chemistry principles, process development, engineering, analytical and purification technologies. * Excellent written and oral communication skills, with a demonstrated ability to effectively collaborate with stakeholders. * In-depth knowledge of pharmaceutical development from pre-clinical to commercialization. * Experience in a regulated pharmaceutical environment including GMP deliveries and regulatory authoring. * This is an on-site designated role, and you will be expected to work five days per week on-site. **PREFERRED QUALIFICATIONS:** * Experience with drug-linkers for ADCs or other targeted mixed-modality therapeutics. * Experience in mentoring scientific staff, both directly and in a matrix environment. * Experience with technology transfer activities and CRO/CDMO resource management. **PHYSICAL/MENTAL REQUIREMENTS:** * Lifting, sitting, standing, walking, bending and ability to perform mathematical calculations. **NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS:** * This is a laboratory-based position. Adherence to safe work practices, including the use of personal protective equipment (PPE) such as safety glasses, lab coats, and gloves, is required. Occasional travel for business purposes, scientific conferences, or collaboration (typically <10%) is expected. **Additional Job Details:** * Work Location Assignment: **On Premise** * Last date to apply is June 23, 2026 The annual base salary for this position ranges from $106,000.00 to $176,600.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 15.0% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits | (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States. Relocation assistance may be available based on business needs and/or eligibility. Candidates must be authorized to be employed in the U.S. by any employer. U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future. **Sunshine Act** Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative. **EEO & Employment Eligibility** Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E-Verify employer. This position requires permanent work authorization in the United States. Pfizer endeavors to make [www.pfizer.com/careers](http://www.pfizer.com/careers) accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and/or interviewing, please email [disabilityrecruitment@pfizer.com](mailto:disabilityrecruitment@pfizer.com). This is to be used solely for accommodation requests with respect to the accessibility of our website, online application process and/or interviewing. Requests for any other reason will not be returned. To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on [ _Pfizer Careers_](https://www.pfizer.com/about/careers/ai-use-guidelines). Research and Development", "summaryDescription" : "Principal Scientist, Process Chemistry at Pfizer. Skills: Process Chemistry, Drug-linker development, ADC therapeutics. Provide technical leadership. Drive development of safe chemistry", "postedDate" : "2026-06-11T16:38:32.738", "createdAt" : "2026-06-11T16:38:32.584", "lastUpdated" : "2026-06-13T17:05:08.145", "ingestTimestamp" : "2026-06-11T16:38:32.584", "mongoId" : "6a2a5dea74d8ee00a061ac00", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://pfizer.wd1.myworkdayjobs.com/PfizerCareers/job/United-States---Washington--Bothell/Principal-Scientist--Process-Chemistry_4958939-1", "salaryAnnualMin" : 106000, "salaryAnnualMax" : 106000, "salaryBand" : "100k-150k", "hasSalary" : true, "allSkills" : [ "Synthetic organic chemistry", "Process development", "Engineering", "Analytical technologies", "Purification technologies", "GMP deliveries", "Regulatory authoring", "Technical leadership", "Mentoring", "Process Chemistry", "Drug-linker development", "ADC therapeutics", "Route screening", "Process characterization", "Scale-up", "GMP manufacturing", "Technology transfer", "Resource management", "Antibody-drug conjugates", "Drug-linkers", "Targeted cancer therapy", "Pharmaceutical development", "Regulated pharmaceutical environment" ], "skillCount" : 23, "jobCategory" : "engineering", "seniorityLevel" : "senior", "dataQualityScore" : 1.0, "_score" : 748.5996, "daysOld" : 2, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Pharmaceutical" }, { "id" : "68e6b1d870f0b99d58d66a7d", "title" : "Principal Applied Scientist, Sponsored Products and Brands", "company" : "Amazon.com Services LLC", "industry" : "Machine Learning Science, Applied Science, Advertising", "salary" : { "min" : 179000.0, "max" : 309400.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Palo Alto, California, USA", "locations" : [ "Palo Alto, California, USA" ], "timestamp" : "2026-06-15T02:43:22.161", "fingerprint" : "8ec624e328332c112fe76bd73d0f93905c4dc4d17289525424262bda6dce27e3", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3103340/apply", "externalId" : "b6d82723-d7c7-4963-b783-09fa12bb7be8", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Palo Alto", "state" : "California", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "37.4443293,-122.1598465" } ], "locationText" : "Palo Alto, California, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "GenAI, Machine learning, Generative AI, LLM", "professionalSkills" : [ "ML strategy", "GenAI strategy", "Recommender systems" ], "hardSkillsList" : [ "Algorithm design", "Complexity analysis", "Mathematics", "Statistics" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Algorithm design", "level" : "intermediate" }, { "name" : "Complexity analysis", "level" : "intermediate" }, { "name" : "Mathematics", "level" : "intermediate" }, { "name" : "Statistics", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "ML, GenAI, Ranking systems, Retrieval systems", "majorSkillsList" : [ "ML", "GenAI", "Ranking systems", "Retrieval systems" ], "coreResponsibilities" : "Define technical and scientific strategies, Influence scientific and engineering methods, Invent new scientific techniques and paradigms, Grow other scientists in the organization, Liaise with internal Amazon partners, Bring state-of-the-art models to production, Stay abreast of the latest developments in AI/ML/GenAI, Identify opportunities to improve efficiency and productivity, Define a long-term science vision, Translate vision into actionable plans, Build complex, real-time systems using GenAI, Develop machine learning platform strategy, Develop a GenAI strategy for recommender systems", "teamCollaboration" : "Multiple teams of scientists and engineers; Cross-functional projects", "domainKnowledge" : "Ranking systems; Retrieval systems; Recommender systems; Advertising landscape; Generative AI technologies", "domainList" : [ "ranking-systems", "retrieval-systems", "recommender-systems", "advertising-landscape", "generative-ai-technologies" ], "domainSpecificKnowledge" : [ "Ranking systems", "Retrieval systems", "Recommender systems", "Advertising landscape", "Generative AI technologies" ], "communicationSkills" : "Verbal communication; Written communication", "innovationCreativity" : "Invent new product experiences; Innovates on behalf of customers; Invent new scientific techniques and paradigms", "problemSolvingSkills" : "Solve complex challenges", "qualificationsBasic" : "PhD or equivalent in Computer science/Math or related field, Experience in building complex, real-time systems using GenAI, Computer Science fundamentals in data structures, algorithm design and complexity analysis, Ability to develop machine learning platform strategy in recommender systems, Ability to develop a GenAI strategy for recommender systems", "qualificationsPreferred" : "10+ years of relevant, broad research experience after PhD degree, Deep expertise and knowledge of Generative AI methods, Proficiency in programming for algorithm and code reviews, Core competency in mathematics and statistics, Track record of successful projects in algorithm design and product development, Publications at top-tier peer-reviewed conferences or journals, Experience with mentorship and/or management of senior scientists and engineers, Effective verbal and written communication skills with non-technical and technical audiences, Experience working with real-world data sets and building scalable models from big data, Demonstrated track record of project delivery for large, cross-functional projects, Ability to take a project from requirements gathering and design to actual product launch", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "The Sponsored Products (SP) team is looking for a Principal Scientist to help our millions of shoppers intuitively navigate our vast inventory by harnessing the power of ML and GenAI models. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, SP helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As a Principal Scientist in Sponsored Products, you will have deep subject matter expertise in the area of ML and GenAI models for ranking and retrieval systems. You will work with multiple teams of scientists and engineers to translate business and functional requirements into concrete deliverables. You will invent new product experiences that enable our shoppers to easily navigate our vast inventory either via search queries, multi-turn conversations, or other modes of input. You will liaise with internal Amazon partners and work on bringing state-of-the-art models to production in the retrieval and relevance ranking models used in Sponsored Products. You will stay abreast of the latest developments in the space of AI/ML/GenAI modeling and identify opportunities to improve the efficiency and productivity of the team. You will define a long-term science vision for our advertising business, driven by our customer’s needs, and translate it into actionable plans for our team of applied scientists, and engineers. Key job responsibilities - You define technical and scientific strategies to make progress on difficult scientific problems that are still unsolved at Amazon. - Your influence scientific and engineering methods across all of Sponsored Product and Sponsored Brand. - You invent new scientific techniques and paradigms to address our customers' needs and business problems. - You are a role model and help grow other scientists in the organization. - You have deep and broad expertise in Artificial Intelligence and GenAI. You draw from scientific and industrial maturity to make strategic judgement calls. - You have 10+ years of research experience after a PhD degree. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Within Sponsored Product and Brands, the Demand Utilization Venture team researches and deploys Generative AI and Reinforcement Learning solutions to improve our understanding of shoppers needs, our understanding of advertisers intent, and how we find the right sponsored products for the right shopper in the right search context. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Basic Qualifications: - PhD or equivalent in Computer science/Math or related field.  - Experience in building complex, real-time systems using GenAI, with successful delivery to customers. - Computer Science fundamentals in data structures, algorithm design and complexity analysis.  - Ability to develop machine learning platform strategy in the domain of recommender systems. - Ability to develop a GenAI strategy for recommender systems. Preferred Qualifications: 10+ years of relevant, broad research experience after PhD degree or equivalent.  Deep expertise and knowledge of Generative AI methods involved in pre/post training, LLM optimizations for performance and throughput. Proficiency in programming for algorithm and code reviews.  Strong core competency in mathematics and statistics.  Track record of successful projects in algorithm design and product development.  Publications at top-tier peer-reviewed conferences or journals.  Experience with mentorship and/or management of senior scientists and engineers.  Effective verbal and written communication skills with non-technical and technical audiences. Experience working with real-world data sets and building scalable models from big data. Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements. Ability to take a project from requirements gathering and design to actual product launch. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.", "summaryDescription" : "Principal Applied Scientist, Sponsored Products and Brands at Amazon.com Services LLC. Skills: ML, GenAI, Ranking systems, Retrieval systems. Define technical and scientific strategies. Influence scientific and engineering methods", "postedDate" : "2026-06-13T06:10:32.841", "createdAt" : "2026-06-13T06:10:32.841", "lastUpdated" : "2026-06-15T02:43:22.161", "ingestTimestamp" : "2026-06-13T06:10:32.841", "mongoId" : "68e6b1d870f0b99d58d66a7d", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3103340/apply", "salaryAnnualMin" : 179000, "salaryAnnualMax" : 309400, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Algorithm design", "Complexity analysis", "Mathematics", "Statistics", "Leadership", "Mentoring", "ML", "GenAI", "Ranking systems", "Retrieval systems", "ML strategy", "GenAI strategy", "Recommender systems", "Advertising landscape", "Generative AI technologies" ], "skillCount" : 15, "jobCategory" : "data", "dataQualityScore" : 1.0, "_score" : 727.89886, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "69fe94ff4c40e2acd1121a1b", "title" : "Principal Applied Scientist, Automated Reasoning Group", "company" : "Amazon Development Center U.S., Inc.", "industry" : "Applied Science, Cloud Computing", "salary" : { "min" : 198900.0, "max" : 295900.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Seattle, Washington, USA", "locations" : [ "Seattle, Washington, USA" ], "timestamp" : "2026-06-15T04:31:43.125", "fingerprint" : "bd70313ba7287ab544965bc045c4f5100a7e8d3f757ef5c5680bd0cb2638eab4", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.jobs/jobs/10414408/apply", "externalId" : "f61b56b1-d9fc-46bd-8fae-ab11b28b634c", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Seattle", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.6038321,-122.3300620" } ], "locationText" : "Seattle, Washington, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "PhD", "experienceMinYears" : 6, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "O'Caml, Dafny, Haskell, Kotlin, Lean, Rust, Scala", "professionalSkills" : [ "Problem solving", "Scientific innovation", "Strategic problem solving", "Technical influence", "Strategic plans" ], "hardSkillsList" : [ "Automated reasoning", "Privacy", "Sovereignty" ], "softSkillsList" : [ "Mentoring" ], "hardSkills" : [ { "name" : "Automated reasoning", "level" : "intermediate" }, { "name" : "Privacy", "level" : "intermediate" }, { "name" : "Sovereignty", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Mentoring" } ], "majorSkills" : "Automated reasoning, Privacy, Sovereignty", "majorSkillsList" : [ "Automated reasoning", "Privacy", "Sovereignty" ], "coreResponsibilities" : "Solve complex problems, Own strategic problem solving, Lead design for solutions, Lead implementation for solutions, Lead delivery for solutions, Provide cross-organizational technical influence, Develop strategic plans, Identify new solutions, Assist in career development, Mentor individuals, Mentor community", "teamCollaboration" : "Cross-organizational technical influence", "domainKnowledge" : "Automated reasoning; Privacy; Sovereignty; Security; Cloud computing", "domainList" : [ "automated-reasoning", "privacy", "sovereignty", "security", "cloud-computing" ], "domainSpecificKnowledge" : [ "Automated reasoning", "Privacy", "Sovereignty", "Security", "Cloud computing" ], "innovationCreativity" : "Scientific innovation", "problemSolvingSkills" : "Problem solving; Strategic problem solving", "outcomesObjectives" : "Long-term quantifiable impact", "qualificationsBasic" : "PhD in Computer Science, PhD in Mathematics, Experience in professional software development, Experience programming in O'Caml, Experience programming in Dafny, Experience programming in Haskell, Experience programming in Kotlin, Experience programming in Lean, Experience programming in Rust, Experience programming in Scala", "qualificationsPreferred" : "Deep understanding of security science, Deep understanding of privacy science, Deep understanding of sovereignty science, Experience distilling informal customer requirements, Experience dealing with ambiguity, Experience dealing with competing objectives, 6+ years experience leading scientists, 6+ years experience leading science teams, 6+ years experience leading science products", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. Basic Qualifications: - PhD in Computer Science, Mathematics, or a related technical field. - Experience in professional software development. - Experience programming in O'Caml, Dafny, Haskell, Kotlin, Lean, Rust, Scala or related language. Preferred Qualifications: - Deep understanding of the science underlying security, privacy, and sovereignty. - Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives. - 6+ years experience leading scientists, science teams, or science products in either academic or industrial setting. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, MA, BOSTON - 198,900.00 - 269,000.00 USD annually USA, MA, Boston - 198,900.00 - 269,000.00 USD annually USA, NY, New York City - 218,800.00 - 295,900.00 USD annually USA, NY, New york city - 218,800.00 - 295,900.00 USD annually USA, OR, PORTLAND - 198,900.00 - 269,000.00 USD annually USA, OR, Portland - 198,900.00 - 269,000.00 USD annually USA, TX, AUSTIN - 198,900.00 - 269,000.00 USD annually USA, TX, Austin - 198,900.00 - 269,000.00 USD annually USA, WA, SEATTLE - 198,900.00 - 269,000.00 USD annually USA, WA, Seattle - 198,900.00 - 269,000.00 USD annually", "summaryDescription" : "Principal Applied Scientist, Automated Reasoning Group at Amazon Development Center U.S., Inc.. Skills: Automated reasoning, Privacy, Sovereignty. Solve complex problems. Own strategic problem solving", "postedDate" : "2026-06-13T15:01:16.636", "createdAt" : "2026-06-13T15:01:16.636", "lastUpdated" : "2026-06-15T04:31:43.125", "ingestTimestamp" : "2026-06-13T15:01:16.636", "mongoId" : "69fe94ff4c40e2acd1121a1b", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.jobs/jobs/10414408/apply", "salaryAnnualMin" : 198900, "salaryAnnualMax" : 295900, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Automated reasoning", "Privacy", "Sovereignty", "Mentoring", "Problem solving", "Scientific innovation", "Strategic problem solving", "Technical influence", "Strategic plans", "Security", "Cloud computing" ], "skillCount" : 11, "jobCategory" : "other", "dataQualityScore" : 1.0, "_score" : 725.3606, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology" }, { "id" : "68c2e420f8b1b0376634c4da", "title" : "Principal Applied Scientist, AWS Agentic AI", "company" : "Amazon Development Center U.S., Inc.", "industry" : "Machine Learning Science, Applied Science, Cloud Computing", "salary" : { "min" : 179000.0, "max" : 309400.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Seattle, Washington, USA", "locations" : [ "Seattle, Washington, USA" ], "timestamp" : "2026-06-15T02:22:46.044", "fingerprint" : "8f0187df86f43df38fa952b01ea9445da857b8a667c652e6d45dc05e058831d0", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3078193/apply", "externalId" : "18c18c46-a13a-40b3-bf50-33b33c88afd3", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Seattle", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.6038321,-122.3300620" } ], "locationText" : "Seattle, Washington, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "PhD", "experienceMinYears" : 8, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Java, C++, Python, AI, Deep Learning, LLM, Graph learning, Static code analysis, Software engineering, NLP, NLU, Multi-modality models", "professionalSkills" : [ "Technology strategy", "Roadmap development", "Product development", "Prototype development", "Technology assessment" ], "hardSkillsList" : [ "Model building", "Deep learning", "LLM training", "Graph learning", "Static code analysis", "Software engineering" ], "softSkillsList" : [ "Mentoring" ], "hardSkills" : [ { "name" : "Model building", "level" : "intermediate" }, { "name" : "Deep learning", "level" : "intermediate" }, { "name" : "LLM training", "level" : "intermediate" }, { "name" : "Graph learning", "level" : "intermediate" }, { "name" : "Static code analysis", "level" : "intermediate" }, { "name" : "Software engineering", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Mentoring" } ], "majorSkills" : "Agentic AI, Deep Learning, LLM, NLP/NLU", "majorSkillsList" : [ "Agentic AI", "Deep Learning", "LLM", "NLP/NLU" ], "coreResponsibilities" : "Develop breakthroughs in agentic architecture, Mentor and guide junior scientists, Develop technology strategy, Develop roadmap supporting product development, Develop prototypes for key technology, Oversee productization of prototype, Develop critical assessment for prototype, Write technical reports summarizing R&D progresses, Communicate R&D progresses with stakeholders", "domainKnowledge" : "Agentic AI; Natural Language Understanding; AI assisted code development; LLM training and fine-tuning; Applied Machine Learning; Conversational AI", "domainList" : [ "agentic-ai", "natural-language-understanding", "ai-assisted-code-development", "llm-training-and-fine-tuning", "applied-machine-learning", "conversational-ai" ], "domainSpecificKnowledge" : [ "Agentic AI", "Natural Language Understanding", "AI assisted code development", "LLM training and fine-tuning", "Applied Machine Learning", "Conversational AI" ], "documentationSkills" : "Technical reports", "innovationCreativity" : "Develop novel algorithms; Develop modeling techniques; Explore new technologies; Find creative solutions", "qualificationsBasic" : "PhD or Master's degree and 10+ years experience, 8+ years building models for business application, Experience in patents or publications, Experience programming in Java, C++, Python, Experience in AI, Deep Learning, LLM training, graph learning, static code analysis, software engineering", "qualificationsPreferred" : "Experience using Unix/Linux, Experience in professional software development", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "AWS Agentic AI is looking for a world-class Applied Scientist with a strong deep learning background to help build industry-leading Agentic AI (models, systems, and applications). Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), AI assisted code development, reasoning and planning with LLMs, LLM training and fine-tuning, and Applied Machine Learning (ML). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in Agentic AI. Your work will directly impact millions of our customers in the form of products and services that make use AI technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of Agentic AI: NLP/NLU, Conversational AI, Reasoning and Planning, LLM model training, and application development. Key job responsibilities - Develop breakthroughs is agentic architecture in areas such as autonomy, self-validation, and continuous learning. - Mentor and guide junior scientists. - Develop technology strategy and roadmap supporting product development - Develop prototypes for key technology, oversee the productization of the prototype, and develop critical assessment for the prototype - Write technical reports summarizing R&D progresses and communicate such progresses with stakeholders About the team The Amazon Web Services (AWS) Agentic AI team uses generative AI and foundation models to advance application development. In the space of software development and life cycle management, we see the application of AI anywhere: from the IDE to web-based tools and services, AI helps engineers work many applications. We explore new technologies and find creative solutions. Curiosity and an explorative mindset can find a place here to impact the life of engineers around the world. If you are excited about this space and want to enlighten your peers with new capabilities, this is the team for you. Basic Qualifications: - PhD, or Master's degree and 10+ years of CS, CE, ML or related field experience - 8+ years of building models for business application experience - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Experience programming in Java, C++, Python or related language - Experience in any of the following areas: AI (NLP or multi-modality models), Deep Learning, LLM model training, graph learning, static code analysis, software engineering. Preferred Qualifications: • Experience using Unix/Linux • Experience in professional software development Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.", "summaryDescription" : "Principal Applied Scientist, AWS Agentic AI at Amazon Development Center U.S., Inc.. Skills: Agentic AI, Deep Learning, LLM, NLP/NLU. Develop breakthroughs in agentic architecture. Mentor and guide junior scientists", "postedDate" : "2026-06-13T15:11:47.462", "createdAt" : "2026-06-13T15:11:47.462", "lastUpdated" : "2026-06-15T02:22:46.044", "ingestTimestamp" : "2026-06-13T15:11:47.462", "mongoId" : "68c2e420f8b1b0376634c4da", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3078193/apply", "salaryAnnualMin" : 179000, "salaryAnnualMax" : 309400, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Model building", "Deep learning", "LLM training", "Graph learning", "Static code analysis", "Software engineering", "Mentoring", "Agentic AI", "Deep Learning", "LLM", "NLP/NLU", "Technology strategy", "Roadmap development", "Product development", "Prototype development", "Technology assessment", "Natural Language Understanding", "AI assisted code development", "LLM training and fine-tuning", "Applied Machine Learning", "Conversational AI" ], "skillCount" : 21, "jobCategory" : "data", "dataQualityScore" : 1.0, "_score" : 719.8491, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "6a3065aff292fd229cfaa802", "title" : "Sales Engineering Enablement, Principal", "company" : "Snowflake", "industry" : "Sales", "salary" : { "min" : 188000.0, "max" : 246700.0, "currency" : "USD", "frequency" : "Yearly" }, "location" : "US-CA-Menlo Park", "locations" : [ "US-CA-Menlo Park" ], "certifications" : [ "SnowPro Core", "SnowPro Advanced" ], "timestamp" : "2026-06-16T10:05:27.975", "fingerprint" : "1277a13dfa41961978352f66fa124f89a52c499d0f5ad01671be278f45df4bc4", "source" : "ashby", "language" : "en", "applyUrl" : "https://jobs.ashbyhq.com/snowflake/2c5d5816-8a14-49b8-b6d7-6ec66b3595b0/application", "externalId" : "2c5d5816-8a14-49b8-b6d7-6ec66b3595b0", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Menlo Park", "state" : "California", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "37.4519671,-122.1779920" } ], "locationText" : "Menlo Park, California, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "technicalSkills" : "OLAP, OLTP, SQL, Python, Snowflake", "professionalSkills" : [ "Business acumen", "Technical acumen", "Sales acumen", "Strategic thinking", "Resourcefulness", "Agility", "Decision-making", "Commitment to excellence" ], "hardSkillsList" : [ "Enablement program design", "Enablement program building", "Enablement program delivery" ], "softSkillsList" : [ "Leadership" ], "hardSkills" : [ { "name" : "Enablement program design", "level" : "intermediate" }, { "name" : "Enablement program building", "level" : "intermediate" }, { "name" : "Enablement program delivery", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" } ], "majorSkills" : "Solution Engineering, Enablement, Data", "majorSkillsList" : [ "Solution Engineering", "Enablement", "Data" ], "coreResponsibilities" : "Act as strategic enablement partner, Diagnose skill gaps, Prescribe targeted interventions, Measure capability change, Curate enablement content, Quality-gate enablement content, Validate AI outputs, Maintain source of truth, Feed quality signals, Diagnose field needs, Classify gaps with data, Prioritize gaps with data, Design activation experiences, Pick right modality, Measure behavior change, Act as connector, Act as translator, Build targeted technical content, Measure field activation, Align to release cadence, Establish with SE leadership, Scale local enablement, Capture what's working, Package for field, Shape onboarding program, Refine onboarding program", "teamCollaboration" : "Key member of leadership team; Cross-functional partners; Global SE Onboarding team", "domainKnowledge" : "Enterprise software; Solution engineering; Data space; Skills database", "domainList" : [ "enterprise-software", "solution-engineering", "data-space", "skills-database" ], "domainSpecificKnowledge" : [ "Enterprise software", "Solution engineering", "Data space", "Skills database" ], "communicationSkills" : "Presentation skills", "qualificationsBasic" : "10 years of related experience, Bachelor’s degree, Deep technical expertise in skills database OLAP/OLTP, Deep technical expertise in SQL, Deep technical expertise in Python, Experienced in designing enablement programs, Experienced in building enablement programs, Experienced in delivering enablement programs, Ability to lead through influence, Ability to establish sponsorship, Ability to secure support from stakeholders, Ability to manage competing priorities, Ability to meet deadlines, Ability to produce quality deliverables, Highly organized, Excellent communication skills, Excellent presentation skills, Experience working in fast-changing environment", "qualificationsPreferred" : "Snowflake experience, SnowPro Core certification, SnowPro Advanced certification, Data science experience, Spark experience", "rawDescription" : "At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is seeking an Enablement Business Partner to serve as the Chief Activation Officer for our Solution Engineering team. Embedded within key leadership teams, you will accelerate field capability tied to business outcomes, owning the full spectrum of how SEs learn, from structured programs to in-the-moment performance support. You translate business problems into capability solutions, designing systems that make the field more capable when and where it matters most. IN THIS ROLE YOU WILL GET TO: - Act as the strategic enablement partner for SE leadership, diagnosing skill gaps tied to business outcomes, prescribing targeted interventions, and measuring whether capability actually changed - Curate and quality-gate enablement content: validate AI outputs for accuracy and audience fit, maintain a source of truth for your domains (theater and product), and feed evidence-based quality signals back to PM and PMM - Diagnose field needs through signals (Gong, Salesforce, theater-level data analysis, leadership conversations, steer co themes, PM roadmap changes, etc), then classify and prioritize gaps with data - Design activation experiences, picking the right modality (hands-on or self-serve lab, AI skill, peer learning, coaching nudge), and measuring behavior change, not just completion. This requires technical building skills. - Act as the connector and translator between Product, Field, Leadership, and the Learner; managing intake, building targeted technical content, measuring whether the field activated on the learning, and aligning to release cadence. - Establish yourself with SE leadership as a key member of their extended leadership team - Scale local enablement to org-wide capability, capturing what's working and packaging it for the rest of the field - Collaborate with the Global SE Onboarding team to shape and continuously refine the everboarding program based on the segment's needs REQUIRED SKILLS: - A minimum of 10 years of related experience with a focus on enterprise software, solution engineering, and enablement in the data space - Bachelor’s degree - Exceptional business, technical, and sales acumen - Should have deep technical expertise in skills database OLAP/OLTP, SQL, and/or Python - Snowflake experience, SnowPro Core, and/or SnowPro Advanced is a plus - Data science or Spark experience is a plus - Experienced in designing, building, and delivering enablement programs - Ability to lead through influence - Ability to establish sponsorship and secure support from a diverse group of internal stakeholders and cross-functional partners to successfully deliver programs - Ability to manage competing priorities across multiple projects, while meeting deadlines and producing quality deliverables - Highly organized with excellent communication skills (including presentation skills) - Experience working in a fast-changing environment that requires strategic thinking, resourcefulness, agility, results-oriented decision-making, and commitment to excellence Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com http://careers.snowflake.com", "summaryDescription" : "Sales Engineering Enablement, Principal at Snowflake. Skills: Solution Engineering, Enablement, Data. Act as strategic enablement partner. Diagnose skill gaps", "createdAt" : "2026-06-16T00:27:58.592", "lastUpdated" : "2026-06-16T10:05:27.975", "ingestTimestamp" : "2026-06-16T00:27:58.592", "mongoId" : "6a3065aff292fd229cfaa802", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 188000, "salaryAnnualMax" : 246700, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Enablement program design", "Enablement program building", "Enablement program delivery", "Leadership", "Solution Engineering", "Enablement", "Data", "Business acumen", "Technical acumen", "Sales acumen", "Strategic thinking", "Resourcefulness", "Agility", "Decision-making", "Commitment to excellence", "Enterprise software", "Solution engineering", "Data space", "Skills database", "SnowPro Core", "SnowPro Advanced" ], "skillCount" : 21, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 717.5877, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.95 }, { "id" : "6a3050b7f292fd229cef44d3", "title" : "Sales Engineering Enablement, Principal", "company" : "Snowflake", "industry" : "Sales", "salary" : { "min" : 188000.0, "max" : 246700.0, "currency" : "USD", "frequency" : "Yearly" }, "location" : "US-CA-Menlo Park", "locations" : [ "US-CA-Menlo Park" ], "certifications" : [ "SnowPro Core", "SnowPro Advanced" ], "timestamp" : "2026-06-16T10:37:56.208", "fingerprint" : "f5da9e6a05e4abbed56590f43a37443dd5b0a6c9a9d49cf43d3fab962c62dd3a", "source" : "ashby", "language" : "en", "applyUrl" : "https://jobs.ashbyhq.com/Snowflake/2c5d5816-8a14-49b8-b6d7-6ec66b3595b0/application", "externalId" : "2c5d5816-8a14-49b8-b6d7-6ec66b3595b0", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Menlo Park", "state" : "California", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "37.4519671,-122.1779920" } ], "locationText" : "Menlo Park, California, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "technicalSkills" : "OLAP, OLTP, SQL, Python, Snowflake", "professionalSkills" : [ "Business acumen", "Technical acumen", "Sales acumen", "Strategic thinking", "Resourcefulness", "Agility", "Decision-making", "Commitment to excellence" ], "hardSkillsList" : [ "Enablement program design", "Enablement program building", "Enablement program delivery" ], "softSkillsList" : [ "Leadership" ], "hardSkills" : [ { "name" : "Enablement program design", "level" : "intermediate" }, { "name" : "Enablement program building", "level" : "intermediate" }, { "name" : "Enablement program delivery", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" } ], "majorSkills" : "Solution Engineering, Enablement, Data", "majorSkillsList" : [ "Solution Engineering", "Enablement", "Data" ], "coreResponsibilities" : "Act as strategic enablement partner, Diagnose skill gaps, Prescribe targeted interventions, Measure capability change, Curate enablement content, Quality-gate enablement content, Validate AI outputs, Maintain source of truth, Feed quality signals, Diagnose field needs, Classify gaps with data, Prioritize gaps with data, Design activation experiences, Pick right modality, Measure behavior change, Act as connector, Act as translator, Build targeted technical content, Measure field activation, Align to release cadence, Establish with SE leadership, Scale local enablement, Capture what's working, Package for field, Shape onboarding program, Refine onboarding program", "teamCollaboration" : "Key member of leadership team; Cross-functional partners; Global SE Onboarding team", "domainKnowledge" : "Enterprise software; Solution engineering; Data space; Skills database", "domainList" : [ "enterprise-software", "solution-engineering", "data-space", "skills-database" ], "domainSpecificKnowledge" : [ "Enterprise software", "Solution engineering", "Data space", "Skills database" ], "communicationSkills" : "Presentation skills", "qualificationsBasic" : "10 years of related experience, Bachelor’s degree, Deep technical expertise in skills database OLAP/OLTP, Deep technical expertise in SQL, Deep technical expertise in Python, Experienced in designing enablement programs, Experienced in building enablement programs, Experienced in delivering enablement programs, Ability to lead through influence, Ability to establish sponsorship, Ability to secure support from stakeholders, Ability to manage competing priorities, Ability to meet deadlines, Ability to produce quality deliverables, Highly organized, Excellent communication skills, Excellent presentation skills, Experience working in fast-changing environment", "qualificationsPreferred" : "Snowflake experience, SnowPro Core certification, SnowPro Advanced certification, Data science experience, Spark experience", "rawDescription" : "At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is seeking an Enablement Business Partner to serve as the Chief Activation Officer for our Solution Engineering team. Embedded within key leadership teams, you will accelerate field capability tied to business outcomes, owning the full spectrum of how SEs learn, from structured programs to in-the-moment performance support. You translate business problems into capability solutions, designing systems that make the field more capable when and where it matters most. IN THIS ROLE YOU WILL GET TO: - Act as the strategic enablement partner for SE leadership, diagnosing skill gaps tied to business outcomes, prescribing targeted interventions, and measuring whether capability actually changed - Curate and quality-gate enablement content: validate AI outputs for accuracy and audience fit, maintain a source of truth for your domains (theater and product), and feed evidence-based quality signals back to PM and PMM - Diagnose field needs through signals (Gong, Salesforce, theater-level data analysis, leadership conversations, steer co themes, PM roadmap changes, etc), then classify and prioritize gaps with data - Design activation experiences, picking the right modality (hands-on or self-serve lab, AI skill, peer learning, coaching nudge), and measuring behavior change, not just completion. This requires technical building skills. - Act as the connector and translator between Product, Field, Leadership, and the Learner; managing intake, building targeted technical content, measuring whether the field activated on the learning, and aligning to release cadence. - Establish yourself with SE leadership as a key member of their extended leadership team - Scale local enablement to org-wide capability, capturing what's working and packaging it for the rest of the field - Collaborate with the Global SE Onboarding team to shape and continuously refine the everboarding program based on the segment's needs REQUIRED SKILLS: - A minimum of 10 years of related experience with a focus on enterprise software, solution engineering, and enablement in the data space - Bachelor’s degree - Exceptional business, technical, and sales acumen - Should have deep technical expertise in skills database OLAP/OLTP, SQL, and/or Python - Snowflake experience, SnowPro Core, and/or SnowPro Advanced is a plus - Data science or Spark experience is a plus - Experienced in designing, building, and delivering enablement programs - Ability to lead through influence - Ability to establish sponsorship and secure support from a diverse group of internal stakeholders and cross-functional partners to successfully deliver programs - Ability to manage competing priorities across multiple projects, while meeting deadlines and producing quality deliverables - Highly organized with excellent communication skills (including presentation skills) - Experience working in a fast-changing environment that requires strategic thinking, resourcefulness, agility, results-oriented decision-making, and commitment to excellence Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com http://careers.snowflake.com", "summaryDescription" : "Sales Engineering Enablement, Principal at Snowflake. Skills: Solution Engineering, Enablement, Data. Act as strategic enablement partner. Diagnose skill gaps", "createdAt" : "2026-06-16T00:25:12.833", "lastUpdated" : "2026-06-16T10:37:56.208", "ingestTimestamp" : "2026-06-16T00:25:12.833", "mongoId" : "6a3050b7f292fd229cef44d3", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 188000, "salaryAnnualMax" : 246700, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Enablement program design", "Enablement program building", "Enablement program delivery", "Leadership", "Solution Engineering", "Enablement", "Data", "Business acumen", "Technical acumen", "Sales acumen", "Strategic thinking", "Resourcefulness", "Agility", "Decision-making", "Commitment to excellence", "Enterprise software", "Solution engineering", "Data space", "Skills database", "SnowPro Core", "SnowPro Advanced" ], "skillCount" : 21, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 717.5877, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.95 }, { "id" : "6a2c6a8a74d8ee00a090c85c", "title" : "Senior / Principal Scientist, ADC Conjugation Process Development", "company" : "Immunome, Inc.", "industry" : "Biotech", "salary" : { "min" : 146000.0, "max" : 188000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Bothell, Washington, United States", "locations" : [ "Bothell", "Washington", "United States" ], "timestamp" : "2026-06-16T01:27:46.638", "fingerprint" : "d2cc8a50652feedeac13c5d3a2129052957e3531e98df4a745b6b6a89ddb58b1", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/immunomeinc/jobs/5253227008", "externalId" : "5253227008", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Bothell", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.7623204,-122.2054035" } ], "locationText" : "Bothell, Washington, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "rawDescription" : "Company Overview Immunome is a clinical-stage targeted oncology company committed to developing first-in-class and best-in-class targeted cancer therapies. We are advancing an innovative portfolio of therapeutics, supported by a leadership team with deep experience in the design, development, and commercialization of cutting-edge therapies, including antibody-drug conjugates. Our pipeline includes varegacestat, an investigational gamma secretase inhibitor for which an NDA has been submitted to the U. S. FDA; IM-1021, a clinical-stage ROR1 ADC; IM-3050, an IND-cleared FAP-targeted radiotherapy; and IM-1617, an IND-cleared solid tumor ADC. We are also advancing a broad portfolio of early-stage ADCs pursuing undisclosed solid tumor targets. Position Overview We are seeking an experienced and motivated Senior/Principal Scientist in ADC Conjugation Process Development to support our growing pipeline. This role will be responsible for the optimization of critical conjugation and purification process parameters for ADC Drug Substance (DS) Development. The preferred candidate will coordinate and execute strategies from milligram to multi-gram scales, including tox/GMP batches, while demonstrating practical expertise in different methodologies and purification strategies for clinical and commercial-stage ADC DS manufacturing. The ideal candidate will have experience with technology transfer, regulatory compliance, and coordination with CDMO partners at GMP manufacturing facilities. A strong working knowledge of different conjugation technologies and cytotoxic linker-payloads is essential, as is experience with standard analytical and characterization approaches used for both biologics and ADCs. Responsibilities Implement and execute ADC Conjugation Process Development (PD) strategies, including Design of Experiment (DOE) and One Factor at a Time (OFAT) approaches for critical reaction parameters. Oversee the progression of ADC batch sizes from milligram to >100 g scales to support not only PD optimization, but analytical and formulation development, DS/DP stability studies, and in vivo experiments (including critical GLP tox studies) for both IND- and BLA-stage activities. Lead the multi-gram scale purification optimization for both antibody intermediates and ADCs via ultrafiltration/diafiltration (UF/DF), and, if required, preparative chromatography or other approaches. Fulfill role as ADC DS subject matter expert (SME) on both internal cross-functional CMC teams and for external CDMO partners to ensure alignment on ADC program goals, deliverables, and objectives. Occasional travel to advise and oversee Development and GMP manufacturing processes will be expected. Prepare essential documentation (SOPs, protocols, and reports) to support tech transfers, GMP activities, and regulatory submissions for clinical development and commercialization. Stay current with emerging trends, innovations, and regulatory requirements for ADC DS, integrating necessary or potentially beneficial features into our internal company strategies and workflows. Lead/support authoring and reviewing of relevant module 3 sections of regulatory submissions (IND, IMPD, BLA, etc.), along with, if necessary, responding to information requests and interfacing with global health authorities. Qualifications A Bachelors, Masters, or Ph. D. degree in Chemistry, Chemical/Biochemical Engineering, Biotechnology, or a related field. A minimum of 5 years of biotech/biopharma industry experience with a Ph.D., 8 years with a Master’s, or 10 years with a Bachelor’s degree. An ADC or Conjugation/Bioconjugation Process Development focus is required. Demonstrated success with the development and scale-up of different ADC conjugation technologies and strategies, including a proven ability to work with a variety of different small molecule linker-payloads and antibody intermediate materials. Expected expertise in relevant laboratory operations and equipment necessary for successful execution of development activities. Prior experience with tech transfer, external CDMO engagement (encompassing both Development and GMP manufacturing activities), and internal coordination with relevant cross-functional teams. Knowledge and Skills A strong understanding of the common conjugation, purification, analytical, and characterization approaches used for ADCs, with a solid foundation in overcoming some of the challenges encountered in these workflows. Expertise in tangential flow filtration (TFF) at the multi-gram scale for the efficient removal of unreacted linker-payloads and to provide a stable ADC DS formulation. Good working knowledge of standard analytical and characterization techniques used for ADCs including, but not limited to, SEC, HIC, and RP U/HPLC chromatographic methods in addition to LC-MS, icIEF, and CE-SDS strategies for biomolecules and related conjugates. Knowledge of GMP standards and regulatory guidelines, with experience supporting IND, INDa, and BLA submissions. The ability to manage multiple projects simultaneously while being able to prioritize and balance hands-on tasks with strategic responsibilities. Exceptional communication skills, with an emphasis on effective collaborations with both internal stakeholders and external partners. Washington State Pay Range $146,000—$188,000 USD E/E/O Immunome, Inc. is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. E-Verify Immunome, Inc. is a participant in E-Verify. Please review the following notices: E-Verify Participation Poster | Right to Work Poster (English) | Right to Work Poster (Spanish).", "summaryDescription" : "Senior / Principal Scientist, ADC Conjugation Process Development at Immunome, Inc.", "postedDate" : "2026-06-14T20:01:49.008", "createdAt" : "2026-06-14T20:01:49.008", "lastUpdated" : "2026-06-16T01:27:46.638", "ingestTimestamp" : "2026-06-14T20:01:49.008", "mongoId" : "6a2c6a8a74d8ee00a090c85c", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 146000, "salaryAnnualMax" : 188000, "salaryBand" : "100k-150k", "hasSalary" : true, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Senior", "dataQualityScore" : 0.8888889, "_score" : 717.4308, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Biotech", "llmIndustryConfidence" : 0.98 }, { "id" : "6a0de91358b93c861eb990aa", "title" : "CLINICAL OPEN HOUSE - CRYSTAL RIVER", "company" : "Tampa General Hospital", "industry" : "Healthcare", "salary" : { "min" : 45000.0, "max" : 65000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for entry-level healthcare roles in Crystal River, Florida, adjusted for industry and company size." }, "location" : "United States-Florida-Crystal River", "locations" : [ "United States-Florida-Crystal River", "
TGH Crystal River (TGHCR)
6201 N Suncoast Boulevard

Crystal River, 34428

" ], "certifications" : [ "Registered Nurse License Issued by Florida or Compact State", "Basic Life Support (BLS) through American Heart Association or American Red Cross", "Clinical Laboratory Technologist License Issued by Florida or Compact State", "Radiologic Technology", "American Registry of Radiologic Technologists (ARRT) (R - Radiography)", "American Registry of Radiologic Technologists (ARRT) (CT - Computerized Tomography)", "Basic Life Support (BLS) from American Heart Association or American Red Cross" ], "timestamp" : "2026-06-15T06:47:27.951", "fingerprint" : "14dadb25e0d8d1afe7d2cca7f5d77eef1b19b700dbebcf948d0c68bff59b72c5", "source" : "taleo", "language" : "en", "applyUrl" : "https://tas-tgh.taleo.net/careersection/ex/jobdetail.ftl?job=678893", "externalId" : "678893", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Crystal River", "state" : "Florida", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "28.9024790,-82.5926012" } ], "locationText" : "Crystal River, Florida, United States", "minimumEducation" : "Associate's", "professionalSkills" : [ "Clinical care", "Patient care" ], "hardSkillsList" : [ "Radiologic Technology", "Medical Laboratory Technology", "Computerized Tomography" ], "hardSkills" : [ { "name" : "Radiologic Technology", "level" : "intermediate" }, { "name" : "Medical Laboratory Technology", "level" : "intermediate" }, { "name" : "Computerized Tomography", "level" : "intermediate" } ], "majorSkills" : "Clinical care, Patient care", "majorSkillsList" : [ "Clinical care", "Patient care" ], "domainKnowledge" : "Emergency Department; ICU; OR; Med Surge; Cath Lab; PACU; Telemetry", "domainList" : [ "emergency-department", "icu", "or", "med-surge", "cath-lab", "pacu", "telemetry" ], "domainSpecificKnowledge" : [ "Emergency Department", "ICU", "OR", "Med Surge", "Cath Lab", "PACU", "Telemetry" ], "documentationSkills" : "Patient charting", "qualificationsBasic" : "Associate's degree Nursing, Registered Nurse License, Basic Life Support (BLS), Associate's degree Respiratory Therapy, Registered Respiratory Therapist, Certified Respiratory Therapist, Associate's Degree Medical Laboratory Technology, Bachelor's Degree Biology, Bachelor's Degree Chemistry, Clinical Laboratory Technologist License, Technical Program Radiologic Technology, Associate's Degree Imaging related field, General Radiographer, Nuclear Medicine Technologist, Radiation Therapy Technologist, American Registry of Radiologic Technologists (ARRT) (R - Radiography), American Registry of Radiologic Technologists (ARRT) (CT - Computerized Tomography)", "qualificationsPreferred" : "CT Technologist, Medical Laboratory Technologists", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Clinical Open House - Crystal River Tampa General Hospital Crystal River is seeking Clinical Nurses, Respiratory Therapists, Radiology Technologists, CT Technologists, Medical Laboratory Technologists and more to join our units throughout the hospital, including - Emergency Department, ICU, OR, Med Surge, Cath Lab, PACU, Telemetry, and more. Come meet our hiring managers in one of our series of sessions and learn more about the amazing opportunities at TGH to expand and grow your career. Tuesday June 16th, 2026 from 10:00am-2:00pm Interested? Follow these steps : 1. Apply here! Create your profile and apply to this job posting. 2. Our Talent Acquisition Team will reach out with an email invitation 3. Come prepared to shine! Interviews will take place at Tampa General Hospital Crystal River , 11537 W Emerald Oaks Drive, Crystal River, FL. Clinical Nurse Associate’s degree Nursing Registered Nurse License Issued by Florida or Compact State Basic Life Support (BLS) through American Heart Association or American Red Cross Respiratory Therapist Associate’s degree Respiratory Therapy Registered Respiratory Therapist Or Certified Respiratory Therapist Medical Laboratory Technologists Associate's Degree Medical Laboratory Technology or Bachelor's Degree Biology or Chemistry Clinical Laboratory Technologist License Issued by Florida or Compact State CT Technologist Technical Program Radiologic Technology or Associate's Degree Imaging related field such as Radiography, Nuclear Medicine, or Radiation Therapy General Radiographer, Nuclear Medicine Technologist, Radiation Therapy Technologist Issued by Florida or Compact State American Registry of Radiologic Technologists (ARRT) (R - Radiography) American Registry of Radiologic Technologists (ARRT) (CT - Computerized Tomography) Basic Life Support (BLS) from American Heart Association or American Red Cross Crystal River", "summaryDescription" : "CLINICAL OPEN HOUSE - CRYSTAL RIVER at Tampa General Hospital. Skills: Clinical care, Patient care", "createdAt" : "2026-06-14T02:02:40.197", "lastUpdated" : "2026-06-15T06:47:27.951", "ingestTimestamp" : "2026-06-14T02:02:40.197", "mongoId" : "6a0de91358b93c861eb990aa", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 45000, "salaryAnnualMax" : 65000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Radiologic Technology", "Medical Laboratory Technology", "Computerized Tomography", "Clinical care", "Patient care", "Emergency Department", "ICU", "OR", "Med Surge", "Cath Lab", "PACU", "Telemetry", "Registered Nurse License Issued by Florida or Compact State", "Basic Life Support (BLS) through American Heart Association or American Red Cross", "Clinical Laboratory Technologist License Issued by Florida or Compact State", "American Registry of Radiologic Technologists (ARRT) (R - Radiography)", "American Registry of Radiologic Technologists (ARRT) (CT - Computerized Tomography)", "Basic Life Support (BLS) from American Heart Association or American Red Cross" ], "skillCount" : 18, "jobCategory" : "healthcare", "jobSubcategory" : "support", "seniorityLevel" : "Entry", "dataQualityScore" : 0.6666667, "_score" : 715.58276, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Healthcare", "llmIndustryConfidence" : 0.98 }, { "id" : "6a305649f292fd229cf26da0", "title" : "Principal Applied Scientist, Agentic Automated Reasoning Group", "company" : "Amazon Development Center U.S., Inc.", "industry" : "Applied Science, Cloud Computing", "salary" : { "min" : 198900.0, "max" : 269000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Boston, Massachusetts, USA", "locations" : [ "Boston, Massachusetts, USA" ], "timestamp" : "2026-06-16T07:23:33.194", "fingerprint" : "dfe17f726bcf19427730cb2fa1c08dd6d2bd8231c05c7eafb2810c408128e4c9", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.jobs/jobs/10449178/apply", "externalId" : "9c0a471e-7f3b-4c3e-9012-49ef7e831caf", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Boston", "state" : "Massachusetts", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "42.3588336,-71.0578303" } ], "locationText" : "Boston, Massachusetts, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "PhD", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking a Principal Applied Scientist with broad interest and expertise in interactive theorem proving, programming language semantics, deductive verification and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities - Define roadmap and lead delivery of AR solutions across multiple customer use cases. - Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. - Use tools spanning from fuzzers, property-based testing to model checkers, and interactive theorem provers to establish program properties. - Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team You will be working with a team of formal verification specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of verified code and tools to accelerate code verification for our customer teams. Basic Qualifications: - PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field - Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives - Experience programming in Java, C++, Python or related language - Experience with leading experienced scientists as well as having a record of developing junior members from academia or industry to a career track in a business environment - 10+ years of industry or academic work in formal verification, program analysis, constraint-solving or theorem proving experience Preferred Qualifications: - 10+ years of relevant work in industry or academia experience - Knowledge of problem solving, algorithm design and complexity analysis - Experience creating novel algorithms and advancing the state of the art - Have peer-reviewed scientific contributions in premier journals and conferences Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, CA, Santa Clara - 228,700.00 - 309,400.00 USD annually USA, MA, Boston - 198,900.00 - 269,000.00 USD annually USA, OR, Portland - 198,900.00 - 269,000.00 USD annually USA, TX, Austin - 198,900.00 - 269,000.00 USD annually", "summaryDescription" : "Principal Applied Scientist, Agentic Automated Reasoning Group at Amazon Development Center U.S., Inc.", "postedDate" : "2026-06-15T00:00:00.000", "createdAt" : "2026-06-15T22:00:17.044", "lastUpdated" : "2026-06-16T07:23:33.194", "ingestTimestamp" : "2026-06-15T22:00:17.044", "mongoId" : "6a305649f292fd229cf26da0", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.jobs/jobs/10449178/apply", "salaryAnnualMin" : 198900, "salaryAnnualMax" : 269000, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 714.9321, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "69fa14be4c40e2acd148dc37", "title" : "Principal Engineer, ML Engineering", "company" : "Amazon Development Center U.S., Inc.", "industry" : "Software Development, Cloud Computing", "salary" : { "min" : 200000.0, "max" : 270600.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Bellevue, Washington, USA", "locations" : [ "Bellevue, Washington, USA" ], "timestamp" : "2026-06-15T05:35:50.585", "fingerprint" : "f910bdd5d07b722f5f913f700be9bd6433cbf2d9f6ce4c4be8b2cf3675917d96", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.jobs/jobs/10411392/apply", "externalId" : "188bb652-2565-4a5b-a1a5-4afe5b33bde1", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Bellevue", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.6144219,-122.1923370" } ], "locationText" : "Bellevue, Washington, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "technicalSkills" : "ML Engineering, Applied AI, LLM, Generative AI", "professionalSkills" : [ "Systems thinking", "Technical strategy", "Science-engineering interface" ], "hardSkillsList" : [ "ML development platform", "Agentic AI development", "Model evaluation", "Model deployment", "Prompt optimization", "Model benchmarking", "Data quality evaluation", "Synthetic data generation" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "ML development platform", "level" : "intermediate" }, { "name" : "Agentic AI development", "level" : "intermediate" }, { "name" : "Model evaluation", "level" : "intermediate" }, { "name" : "Model deployment", "level" : "intermediate" }, { "name" : "Prompt optimization", "level" : "intermediate" }, { "name" : "Model benchmarking", "level" : "intermediate" }, { "name" : "Data quality evaluation", "level" : "intermediate" }, { "name" : "Synthetic data generation", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "ML Engineering, Agentic AI development, Model evaluation, Technical strategy", "majorSkillsList" : [ "ML Engineering", "Agentic AI development", "Model evaluation", "Technical strategy" ], "coreResponsibilities" : "Build production-grade 1P agents, Design evaluation frameworks, Create recommendation systems, Drive science-engineering interface, Translate research into production systems, Establish technical strategy, Prototype, scientists, Write production code, Review designs across multiple teams, Design agent architectures, Manage memory, Optimize prompts, Manage tool use, Orchestrate agentic systems, Own evaluation strategy, Develop LLM-as-Judge frameworks, Automate benchmarking, Assess model quality, Build ML-driven recommendation systems, Benchmark models, Select models, Deploy models, Customize models, Establish data quality pipelines, Generate synthetic data, Support model training, Support fine-tuning, Drive requirements, Develop technical roadmap, Translate research prototypes, Create engineering specifications, Define ML engineering strategy, Establish best practices, Mentor senior engineers, Conduct design reviews, Conduct technical deep-dives, Conduct bar-raising interviews, Present technical strategy, Present roadmap", "teamCollaboration" : "Cross-cutting leadership role; Work with science teams; Influence without authority; Work across multiple teams; Cross-organizational technical forums", "projectManagementSkills" : [ "Roadmap planning" ], "domainKnowledge" : "AI Platforms; ML development platform; Agentic AI development; LLM-as-Judge; Model benchmarking; Synthetic data generation; Generative AI systems", "domainList" : [ "ai-platforms", "ml-development-platform", "agentic-ai-development", "llm-as-judge", "model-benchmarking", "synthetic-data-generation", "generative-ai-systems" ], "domainSpecificKnowledge" : [ "AI Platforms", "ML development platform", "Agentic AI development", "LLM-as-Judge", "Model benchmarking", "Synthetic data generation", "Generative AI systems" ], "communicationSkills" : "Present technical strategy; Present roadmap", "innovationCreativity" : "Translate innovative research", "problemSolvingSkills" : "Systems thinking", "outcomesObjectives" : "Deliver superior developer experiences", "qualificationsBasic" : "Experience in software development, Experience designing and shipping production-grade ML systems, Experience with model evaluation, Experience with recommendation systems, Demonstrated ability to work across multiple teams", "qualificationsPreferred" : "Master’s or PhD in Machine Learning, Experience building production LLM-powered agents, Experience with LLM-as-Judge, Experience with prompt engineering, Experience with synthetic data generation, Track record of driving science-engineering collaboration, Experience leading technical strategy, Publications, patents, or significant open-source contributions in ML/AI", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "We are looking for a Principal Engineer with deep ML engineering expertise to lead the ML and science engineering effort across the AI Platforms organization at AWS. This is a cross-cutting leadership role spanning the full breadth of our ML development platform: data preparation, model evaluation, model deployment and customization, and agentic AI development experience. In this role, you will be responsible for building production-grade 1P agents, designing comprehensive evaluation frameworks (including LLM-as-Judge), creating recommendation systems for model benchmarking and selection, and driving the science-engineering interface to deliver superior developer experiences at AWS scale. You will work closely with world-class science teams to translate innovative research into production systems and establish the technical strategy for ML engineering across the organization. You are not just an architect — you are a hands-on builder who can prototype alongside scientists, write production code, and review designs across multiple teams. You bring a rare combination of ML depth, systems thinking, and the ability to influence without authority at a global scale. Key job responsibilities • Design and build production-grade 1P agent architectures including memory management, prompt optimization, tool use, and agentic orchestration systems • Define and own the evaluation strategy for AI Platforms, including LLM-as-Judge frameworks, automated benchmarking, and model quality assessment pipelines • Build ML-driven recommendation systems for model benchmarking, selection, deployment, and customization from Model Hub • Establish data quality evaluation pipelines and synthetic data generation infrastructure to support model training and fine-tuning at scale • Drive requirements and technical roadmap with the science team; translate research prototypes into engineering specifications and production systems • Define the ML engineering technical strategy across AI Platforms, establishing best practices for agent development, model evaluation, and science-to-production pipelines • Mentor senior engineers across the organization; conduct design reviews, technical deep-dives, and bar-raising interviews • Present technical strategy and roadmap to VP-level stakeholders; represent AI Platforms ML engineering in cross-organizational technical forums Basic Qualifications: • Experience in software development with a significant focus on ML engineering or applied AI systems • Experience designing and shipping production-grade ML systems or AI agents that operate at scale (not just prototype or research systems) • Experience with model evaluation, benchmarking, or quality assessment of ML/AI systems in a production environment • Experience with recommendation systems, retrieval systems, or similar ML systems running in production • Demonstrated ability to work across multiple teams as a technical leader, influencer, or architect Preferred Qualifications: • Master’s or PhD in Machine Learning, Artificial Intelligence, Computer Science, or a related quantitative field • Experience building production LLM-powered agents including memory management, prompt optimization, tool use, and multi-step reasoning • Experience with LLM-as-Judge, automated evaluation methodologies, or building evaluation infrastructure for generative AI systems • Experience with prompt engineering and prompt optimization at scale (beyond single-model, to fleet-scale optimization) • Experience with synthetic data generation, data quality evaluation, or data labeling pipelines for ML model training • Track record of driving science-engineering collaboration: working with research scientists, interpreting research outputs, and translating findings into production systems • Experience leading technical strategy across multiple engineering teams or sub-organizations • Publications, patents, or significant open-source contributions in ML/AI (NeurIPS, ICML, ICLR, KDD, RecSys, EMNLP, ACL, or equivalent venues preferred) Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, WA, BELLEVUE - 200,100.00 - 270,600.00 USD annually", "summaryDescription" : "Principal Engineer, ML Engineering at Amazon Development Center U.S., Inc.. Skills: ML Engineering, Agentic AI development, Model evaluation, Technical strategy. Build production-grade 1P agents. Design evaluation frameworks", "postedDate" : "2026-06-13T14:07:40.070", "createdAt" : "2026-06-13T14:07:40.070", "lastUpdated" : "2026-06-15T05:35:50.585", "ingestTimestamp" : "2026-06-13T14:07:40.070", "mongoId" : "69fa14be4c40e2acd148dc37", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.jobs/jobs/10411392/apply", "salaryAnnualMin" : 200000, "salaryAnnualMax" : 270600, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "ML development platform", "Agentic AI development", "Model evaluation", "Model deployment", "Prompt optimization", "Model benchmarking", "Data quality evaluation", "Synthetic data generation", "Leadership", "Mentoring", "ML Engineering", "Technical strategy", "Systems thinking", "Science-engineering interface", "Roadmap planning", "AI Platforms", "LLM-as-Judge", "Generative AI systems" ], "skillCount" : 18, "jobCategory" : "engineering", "dataQualityScore" : 1.0, "_score" : 713.6915, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology" }, { "id" : "6a25145374d8ee00a00bb357", "title" : "Principal Applied Scientist, AWS Agentic AI", "company" : "Amazon Development Center U.S., Inc.", "industry" : "Applied Science, Cloud Computing", "salary" : { "min" : 218800.0, "max" : 295900.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "New York, New York, USA", "locations" : [ "New York, New York, USA" ], "timestamp" : "2026-06-15T07:46:08.055", "fingerprint" : "a354eb18730e0915a334b9f95e73ad9e7db6bd2c555035ffcd69e1a001ba3164", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.jobs/jobs/10441249/apply", "externalId" : "f62190a5-e1bd-4c29-bf61-f9b66904fe02", "industryFunction" : { }, "structuredLocations" : [ { "city" : "New York", "state" : "New York", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "40.7127281,-74.0060152" } ], "locationText" : "New York, New York, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "Master's", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, Machine Learning, Generative AI, Foundation models, LLMs, Computer vision, Agentic AI", "professionalSkills" : [ "Science leadership", "Research and development", "System architecture" ], "hardSkillsList" : [ "Model training", "Fine-tuning LLMs", "Multi-modal models", "Complex reasoning", "Workflow automation" ], "softSkillsList" : [ "Leadership", "Mentoring", "Strategic thinking" ], "hardSkills" : [ { "name" : "Model training", "level" : "intermediate" }, { "name" : "Fine-tuning LLMs", "level" : "intermediate" }, { "name" : "Multi-modal models", "level" : "intermediate" }, { "name" : "Complex reasoning", "level" : "intermediate" }, { "name" : "Workflow automation", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" }, { "name" : "Strategic thinking" } ], "majorSkills" : "Generative AI, Agentic AI, Foundation models, LLMs", "majorSkillsList" : [ "Generative AI", "Agentic AI", "Foundation models", "LLMs" ], "coreResponsibilities" : "Build multi-modal foundation models, Optimize multi-modal foundation models, Train state-of-the-art LLMs, Fine-tune state-of-the-art LLMs, Architect systems that scale efficiently", "teamCollaboration" : "Deep collaboration with engineering teams", "domainKnowledge" : "Enterprise AI; AI safety; Hallucination mitigation; Retrieval-augmented generation", "domainList" : [ "enterprise-ai", "ai-safety", "hallucination-mitigation", "retrieval-augmented-generation" ], "domainSpecificKnowledge" : [ "Enterprise AI", "AI safety", "Hallucination mitigation", "Retrieval-augmented generation" ], "communicationSkills" : "Communicate complex technical topics", "innovationCreativity" : "Hands-on innovation", "problemSolvingSkills" : "Complex reasoning", "outcomesObjectives" : "Enable intelligent agents; Automate multi-step workflows; Make enterprise users productive", "qualificationsBasic" : "Master's degree with 5+ years experience, Industry experience developing ML models, Experience with generative AI, Proven record of publications or patents, Proficiency in Python, Experience in NLP, LLMs, computer vision, or Agentic AI", "qualificationsPreferred" : "Experience applying generative AI to enterprise tasks, Experience applying generative AI to multi-modal tasks, Understanding of agentic architectures, Understanding of autonomous systems, Understanding of task orchestration, Hands-on experience with scalable ML infrastructure, Hands-on experience with distributed training, Hands-on experience with optimization of large models, Deep knowledge of AI safety, Deep knowledge of hallucination mitigation, Deep knowledge of retrieval-augmented generation, Experience mentoring junior scientists, Experience influencing cross-functional stakeholders, Ability to think strategically, Ability to communicate complex technical topics, Track record of shipping scientific innovations", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Amazon Web Services (AWS) is looking for a Principal Applied Scientist to join the Quick Science team. Quick is AWS’s enterprise generative AI assistant that helps users answer questions, summarize documents, generate content, take actions, and automate workflows using information across enterprise systems. As a key member of this team, you will lead research and development efforts in generative AI and Agentic AI to enable intelligent agents that perform complex reasoning, automate multi-step workflows, and make enterprise users significantly more productive. Key job responsibilities You’ll work on building and optimizing multi-modal foundation models, training and fine-tuning state-of-the-art LLMs, and architecting systems that scale efficiently across domains. This role blends science leadership, hands-on innovation, and deep collaboration with engineering teams to bring research into production. Basic Qualifications: - PhD in Machine Learning, Computer Science, Electrical Engineering, or a related technical field OR a Master’s degree with 5+ years of relevant industry or research experience. - Industry experience developing machine learning models for real-world applications. - Experience with generative AI, including model training or building systems with pre-trained foundation models. - Proven record of peer-reviewed publications or granted patents in AI/ML. - Proficiency in Python or similar programming languages. - Experience in at least one of the following areas: natural language processing (NLP), large language models (LLMs), computer vision, or Agentic AI. Preferred Qualifications: - Experience applying generative AI to enterprise or multi-modal tasks (e.g., code generation, document understanding, or task planning). - Strong understanding of agentic architectures, autonomous systems, or task orchestration. - Hands-on experience with scalable ML infrastructure, distributed training, or optimization of large models. - Deep knowledge of AI safety, hallucination mitigation, or retrieval-augmented generation (RAG). - Experience mentoring junior scientists and influencing cross-functional stakeholders. - Ability to think strategically and communicate complex technical topics to non-experts, including senior leadership. - Track record of shipping scientific innovations into customer-facing products at scale. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, CA, Santa Clara - 228,700.00 - 309,400.00 USD annually USA, NY, New York - 218,800.00 - 295,900.00 USD annually USA, WA, Seattle - 198,900.00 - 269,000.00 USD annually", "summaryDescription" : "Principal Applied Scientist, AWS Agentic AI at Amazon Development Center U.S., Inc.. Skills: Generative AI, Agentic AI, Foundation models, LLMs. Build multi-modal foundation models. Optimize multi-modal foundation models", "postedDate" : "2026-06-13T10:45:40.484", "createdAt" : "2026-06-13T10:45:40.484", "lastUpdated" : "2026-06-15T07:46:08.055", "ingestTimestamp" : "2026-06-13T10:45:40.484", "mongoId" : "6a25145374d8ee00a00bb357", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.jobs/jobs/10441249/apply", "salaryAnnualMin" : 218800, "salaryAnnualMax" : 295900, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "Model training", "Fine-tuning LLMs", "Multi-modal models", "Complex reasoning", "Workflow automation", "Leadership", "Mentoring", "Strategic thinking", "Generative AI", "Agentic AI", "Foundation models", "LLMs", "Science leadership", "Research and development", "System architecture", "Enterprise AI", "AI safety", "Hallucination mitigation", "Retrieval-augmented generation" ], "skillCount" : 19, "jobCategory" : "other", "dataQualityScore" : 1.0, "_score" : 712.8286, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "6a3079aef292fd229c0343d8", "title" : "Principal Software Engineer, Data Engineering", "company" : "Highspot", "industry" : "Engineering", "salary" : { "min" : 250000.0, "max" : 350000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Principal Software Engineer in Seattle, adjusted for the SaaS industry and executive seniority level." }, "location" : "Seattle", "locations" : [ "Seattle" ], "timestamp" : "2026-06-16T15:08:38.625", "fingerprint" : "97b85fa49e6386a6f9a2114f90be281b7e32495458e8d358ee1541ddb99484ec", "source" : "lever", "language" : "en", "applyUrl" : "https://jobs.lever.co/highspot/c49beaf1-a5fe-4883-865a-d8885fbef5df/apply", "externalId" : "c49beaf1-a5fe-4883-865a-d8885fbef5df", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Seattle", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.6038321,-122.3300620" } ], "locationText" : "Seattle, Washington, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 10, "experienceMaxYears" : 99, "experienceLevel" : "Principal", "remoteFriendly" : true, "workSiteFlexibility" : "Flexible", "rawDescription" : "## Description About Highspot Highspot is pioneering the category that is fundamentally changing the way companies increase sales productivity. On a mission to transform the way millions of people work with sales enablement, Highspot is committed to building breakthrough software with a spark of magic. We believe a great place to work is about more than the work – it’s about what the company stands for, and how it authentically represents its values in the real world. To this end, we have put intentional focus on creating equitable workspaces for each of our employees. Our goal is to create a culture where everyone feels a deep sense of belonging and is empowered to be an agent of change, with the ability to transform themselves, their workplace, and their world. About the Role We are looking for a Principal Data Engineer to join our growing Data Platform team. This is a rare opportunity to define the technical vision for high-scale data products that power customer-facing analytics, intelligent AI agents, and core product capabilities. Highspot's data engineering challenges are uniquely interesting: our platform generates rich, deeply nested document-oriented data from millions of enterprise interactions – content engagement, CRM activity, sales workflows – and our job is to make all of it available, trustworthy, and fast for radically different consumption patterns. Customer-facing analytics dashboards need pre-aggregated, low-latency reads. Scorecards and reports need flexible dimensional models. AI agents need fresh, contextually assembled data with retrieval characteristics that look nothing like traditional BI. You'll own the architecture that serves all three from a unified foundation. As a Principal Data Engineer, you will shape our overarching data architecture direction and fundamentally influence our data strategy. Your impact will extend well beyond your immediate crew – acting as a vital bridge that coordinates directly with upstream software engineering teams (data producers) as well as downstream engineering and AI teams (data consumers) across multiple organizational pillars. You will lead the technical execution of a high-performing engineering crew, balancing deep architectural pipeline design with advanced data modeling, query optimization, and data trust. ## What You'll Do Architect the data platform – drive the technical direction for a scalable, reliable data platform built on a medallion architecture that serves customer-facing analytics, reporting, and agentic AI from a unified foundation. Build and optimize ingestion pipelines – design robust CDC, real-time streaming (Kafka, Flink), and batch processing pipelines that transform complex, nested document-oriented operational data into clean analytical models at enterprise scale. Tame schema complexity – build resilient ingestion and transformation layers that gracefully handle deeply nested, continuously evolving document schemas — deciding where to absorb complexity (ingestion, transformation, or query time) and making those tradeoffs explicit and sustainable. Serve AI and analytics consumption patterns – architect data products that support both traditional BI workloads (pre-aggregated dashboards, dimensional models for scorecards and reports) and emerging AI consumption patterns (low-latency retrieval, contextual assembly, freshness-sensitive agent queries). Own data quality, contracts, and observability – establish the data trust infrastructure that makes cross-team data consumption reliable: schema contracts with upstream producers, data quality monitoring, lineage tracking, freshness SLAs, and clear escalation paths when things break. Drive cost-aware architecture – own Snowflake warehouse optimization, compute governance, and cost-efficient pipeline design. Build the practices and visibility so the team makes principled cost/performance tradeoffs rather than discovering them on the invoice. Bridge producers and consumers – collaborate across organizational boundaries to align upstream software engineering teams and downstream analytics and AI teams around unified data strategies, shared contracts, and engineering standards. Lead and grow the team – technically lead and growth-coach a diverse crew of data engineers. Champion best practices across the full spectrum of data engineering disciplines, from low-level pipeline architecture to sophisticated data modeling and analytical query performance. ## Your Background What will set you apart: Demonstrated depth in building production data platforms that serve multiple consumption patterns – you've gone beyond traditional BI to support real-time product features, AI/ML workloads, or customer-facing analytics from the same data foundation. Deep experience with the impedance mismatch between document-oriented operational stores and analytical systems – you've dealt with nested, schema-evolving source data (MongoDB, DynamoDB, or similar) and have opinions on where flattening and transformation should live. Hands-on experience with data quality and trust at scale – you've built or operated schema registries, data contracts, quality monitoring, or lineage systems in an environment where multiple teams depend on shared data products. Track record of cost-conscious data architecture – you've optimized Snowflake (or comparable) warehouse spend, designed compute governance policies, or re-architected pipelines to materially reduce cost without sacrificing reliability. Strong instinct for the bridge role: you're as comfortable pushing back on an upstream team's schema change as you are negotiating freshness SLAs with a downstream AI consumer. Foundations: 8+ years of professional software engineering experience, with significant time spent on distributed, data-intensive production systems – including substantial depth in data pipeline and platform architecture. Deep hands-on expertise with modern data technologies: Snowflake, Apache Kafka, Apache Flink, and CDC tooling (Debezium or similar). Experience developing and operating cloud data infrastructure at enterprise scale (AWS preferred), including infrastructure-as-code (Terraform) and CI/CD automation. Strong programming skills in Python, Java, and SQL. You write production-grade code, not just scripts. A track record of designing performant data models that support fast, efficient querying for analytical and product-facing use cases. Strong cross-functional communication skills - you work effectively with software engineers, data scientists, AI teams, and business stakeholders across organizational boundaries. Experience mentoring engineers and building collaborative, high-performing teams. ## Additional Information Base salary range: $188,696 - $258,391 Employees may also be eligible for bonuses and other forms of compensation. The above represents total expected compensation for this role. Actual compensation will depend on various job-related factors, including, but not limited to, location, experience, and job qualifications. Highspot also offers the following employee benefits for this position: -Comprehensive medical, dental, vision, disability, and life benefits -Health Savings Account (HSA) with employer contribution -401(k) Matching with immediate vesting on employer match -Flexible PTO -8 paid holidays and 5 paid days for Annual Holiday Week -Quarterly Recharge Fridays (paid days off for mental health recharge) -18 weeks paid parental leave -Access to Coaches and Therapists through Modern Health -2 volunteer days per year -Commuting benefits #LI-DL1 Equal Opportunity Statement We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of age, ancestry, citizenship, color, ethnicity, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or invisible disability status, political affiliation, veteran status, race, religion, or sexual orientation. Did you read the requirements as a checklist and not tick every box? Don't rule yourself out! If this role resonates with you, hit the ‘apply’ button.", "summaryDescription" : "Principal Software Engineer, Data Engineering at Highspot", "postedDate" : "2026-06-15T18:22:09.012", "createdAt" : "2026-06-15T18:22:09.012", "lastUpdated" : "2026-06-16T15:08:38.625", "ingestTimestamp" : "2026-06-16T03:26:02.807", "mongoId" : "6a3079aef292fd229c0343d8", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 250000, "salaryAnnualMax" : 350000, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 705.3178, "daysOld" : 0, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "SaaS", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2af78374d8ee00a073d64d", "title" : "(Associate) Principal Bioanalysis Scientist", "company" : "AstraZeneca", "industry" : "Biotechnology", "salary" : { "min" : 650000.0, "max" : 1100000.0, "currency" : "CNY", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market data for a Lead Principal Bioanalysis Scientist in the Biotech industry in China, adjusted for seniority and company size." }, "location" : "China", "locations" : [ "China" ], "timestamp" : "2026-06-14T22:39:17.938", "fingerprint" : "73adc78593bd327430f14655fc74a026788fe151a9e9658f37b66ea2561a58b3", "source" : "workday", "language" : "en", "applyUrl" : "https://astrazeneca.wd3.myworkdayjobs.com/Careers/job/Shanghai-JingAn-Office/XMLNAME--Associate--Principal-Bioanalysis-Scientist_R-254201", "externalId" : "R-254201", "industryFunction" : { }, "locationPoint" : "35.8617,104.1954", "structuredLocations" : [ { "country" : "China", "continent" : "as", "workMode" : "onsite" } ], "locationText" : "China", "employmentType" : "FULL_TIME", "technicalSkills" : "PCR, LBA, ELISA, MSD, Flow cytometry, ddPCR, qPCR, VCN, RCL", "professionalSkills" : [ "Bioanalytical studies", "Lab management", "Method validation", "Regulatory filing" ], "hardSkillsList" : [ "Assay development", "Assay validation", "Sample testing", "Data review", "GLP/GCP compliance" ], "softSkillsList" : [ "Mentoring" ], "hardSkills" : [ { "name" : "Assay development", "level" : "intermediate" }, { "name" : "Assay validation", "level" : "intermediate" }, { "name" : "Sample testing", "level" : "intermediate" }, { "name" : "Data review", "level" : "intermediate" }, { "name" : "GLP/GCP compliance", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Mentoring" } ], "majorSkills" : "Bioanalysis, Assay development, GxP environment", "majorSkillsList" : [ "Bioanalysis", "Assay development", "GxP environment" ], "coreResponsibilities" : "Lead clinical bioanalysis projects, Develop PCR-based assays, Develop LBA assays, Develop flow cytometry assays, Qualify PCR-based assays, Qualify LBA assays, Qualify flow cytometry assays, Validate PCR-based assays, Validate LBA assays, Validate flow cytometry assays, Implement PCR-based assays, Implement LBA assays, Implement flow cytometry assays, Manage in-house bioanalytical studies, Manage outsourced bioanalytical studies, Collaborate with internal teams, Collaborate with CROs, Support lab management, Optimize lab operations, Expand bioanalytical capability, Collaborate with US/UK bioanalytical teams, Transfer PK/IMG/PDiomarker assays, Troubleshoot PK/IMG/PDiomarker assays, Validate PK/IMG/PDiomarker assays, Test PK/IMG/PDiomarker samples, Report PK/IMG/PDiomarker data, Design experiments, Execute experiments, Review experiments, Develop ddPCR methods, Develop qPCR methods, Develop LBA methods, Develop flow cytometry methods, Validate ddPCR methods, Validate qPCR methods, Validate LBA methods, Validate flow cytometry methods, Review sample analysis data, Approve sample analysis data, Review method validation protocols, Approve method validation protocols, Review method validation reports, Approve method validation reports, Review bioanalytical sample testing reports, Approve bioanalytical sample testing reports, Review NDALA dossier sections, Approve NDALA dossier sections, Review SOPs, Approve SOPs, Mentor junior scientists, Present results at team/department meetings, Prepare controlled documents, Prepare study plans, Prepare reports, Contribute to regulatory submissions, Manage resources, Manage timelines, Ensure on-schedule delivery", "teamCollaboration" : "Internal teams; CROs; US/UK bioanalytical teams", "projectManagementSkills" : [ "Manage multiple projects", "Manage timelines" ], "domainKnowledge" : "Cell therapy projects; Biotherapeutic development; Regulatory filing; GxP environment; ICH M10 guidelines; FDA guidelines; EMA guidelines; GLP; GCP; NDALA submission", "domainList" : [ "cell-therapy-projects", "biotherapeutic-development", "regulatory-filing", "gxp-environment", "ich-m10-guidelines", "fda-guidelines", "ema-guidelines", "glp", "gcp", "ndala-submission" ], "domainSpecificKnowledge" : [ "Cell therapy projects", "Biotherapeutic development", "Regulatory filing", "GxP environment", "ICH M10 guidelines", "FDA guidelines", "EMA guidelines", "GLP", "GCP", "NDALA submission" ], "toolProficiency" : [ "MSD" ], "communicationSkills" : "Written communication; Oral communication; English; Chinese; Present results", "documentationSkills" : "Method validation protocols; Method validation reports; Bioanalytical sample testing reports; NDALA dossier sections; SOPs; Controlled documents; Study plans; Reports", "problemSolvingSkills" : "Troubleshooting", "qualificationsBasic" : "Degree in Biology, Biochemistry, Pharmacology, or related field, Experience in pharmaceuticals or biotech, Hands-on experience developing and validating LBA (ELISA, MSD), ddPCR/qPCR (VCN, RCL), and multi-parameter flow cytometry assays (cell counting, immunophenotyping, functional/intracellular staining) in a GxP environment, Experience with bioanalytical method validation per ICH M10, FDA, and EMA guidelines, Written and oral communication skills in English and Chinese, Ability to manage multiple projects and meet timelines", "qualificationsPreferred" : "B. Sc. with 10+ years of pharma/biotech experience, M. Sc. with 8+ years of pharma/biotech experience, Ph. D. with 6+ years of pharma/biotech experience, Experience with single-cell sequencing, Bioinformatic QC experience, Translational genomics experience, Familiarity with NDALA submission requirements, Experience overseeing CRO bioanalytical activities", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "The Bioanalysis Laboratory supports AstraZeneca R&D China by generating quality-compliant PK, PD, ADA, NAb, and biomarker data for cell therapy projects, biotherapeutic development and regulatory filing. The Associate Principal or Principal Bioanalysis Scientist is responsible for: * Leading clinical bioanalysis projects as principal investigator: development, qualification/validation, and implementation of PCR-based (VCN, RCL), LBA (ELISA, MSD)and flow cytometry assays in a GxP environment. * Managing in-house and outsourced bioanalytical studies in collaboration with internal teams and CROs. * Supporting lab management to optimize operations and expand bioanalytical capability. The job title and grade offered will be commensurate with the successful candidate's experience and qualifications **Typical Accountabilities** * Collaborate with US/UK bioanalytical teams for PK/IMG/PD/Biomarker assay transfer, troubleshooting, validation, sample testing, and reporting. * Design, execute, and review experiments for development and validation of ddPCR or qPCR (VCN, RCL), LBA (ELISA, MSD), and flow cytometry (absolute counting, immunophenotyping, receptor occupancy, functional assays) methods. * Review and approve sample analysis data, method validation protocols/reports, bioanalytical sample testing reports, NDA/BLA dossier sections, and SOPs in compliance with GLP/GCP requirements. * Mentor junior scientists on experiment planning, troubleshooting, and data interpretation. * Present results at team/department meetings; prepare controlled documents, study plans, reports, and regulatory submission contributions. * Manage resources and timelines to ensure on-schedule delivery. Essential * Degree in Biology, Biochemistry, Pharmacology, or related field. * Experience in pharmaceuticals or biotech. * Hands-on experience developing and validating LBA (ELISA, MSD), ddPCR/qPCR (VCN, RCL), and multi-parameter flow cytometry assays (cell counting, immunophenotyping, functional/intracellular staining) in a GxP environment. * Experience with bioanalytical method validation per ICH M10, FDA, and EMA guidelines. * Written and oral communication skills in English and Chinese. * Ability to manage multiple projects and meet timelines. Desirable * B.Sc. with 10+ years, M.Sc. with 8+ years, or Ph.D. with 6+ years of pharma/biotech experience. * Experience with single-cell sequencing, bioinformatic QC, or translational genomics is a plus. * Familiarity with NDA/BLA submission requirements. * Experience overseeing CRO bioanalytical activities (method transfer, audit support, data review). **Date Posted** 10-6月-2026 **Closing Date** 30-12月-2026 AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.", "summaryDescription" : "(Associate) Principal Bioanalysis Scientist at AstraZeneca. Skills: Bioanalysis, Assay development, GxP environment. Lead clinical bioanalysis projects. Develop PCR-based assays", "postedDate" : "2026-06-11T05:06:02.651", "createdAt" : "2026-06-12T05:06:02.636", "lastUpdated" : "2026-06-14T22:39:17.938", "ingestTimestamp" : "2026-06-12T05:06:02.636", "mongoId" : "6a2af78374d8ee00a073d64d", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://astrazeneca.wd3.myworkdayjobs.com/Careers/job/Shanghai-JingAn-Office/XMLNAME--Associate--Principal-Bioanalysis-Scientist_R-254201", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Assay development", "Assay validation", "Sample testing", "Data review", "GLP/GCP compliance", "Mentoring", "Bioanalysis", "GxP environment", "Bioanalytical studies", "Lab management", "Method validation", "Regulatory filing", "Manage multiple projects", "Manage timelines", "Cell therapy projects", "Biotherapeutic development", "ICH M10 guidelines", "FDA guidelines", "EMA guidelines", "GLP", "GCP", "NDALA submission", "MSD" ], "skillCount" : 23, "jobCategory" : "engineering", "seniorityLevel" : "senior", "dataQualityScore" : 0.8888889, "_score" : 704.6714, "daysOld" : 3, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Biotechnology" }, { "id" : "69fda4e34c40e2acd1b9974a", "title" : "Principal Applied Scientist, Sponsored Products and Brands", "company" : "Amazon.com Services LLC", "industry" : "Applied Science, Advertising", "salary" : { "min" : 228700.0, "max" : 309400.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Palo Alto, California, USA", "locations" : [ "Palo Alto, California, USA" ], "timestamp" : "2026-06-15T06:34:06.169", "fingerprint" : "e48365b86c403c615a246893e6332d35e97417f8226f9370a866d50001d31b19", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.jobs/jobs/10413683/apply", "externalId" : "486289d8-494c-400a-8e28-f2291fbb0c9e", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Palo Alto", "state" : "California", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "37.4443293,-122.1598465" } ], "locationText" : "Palo Alto, California, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "AI/ML, GenAI, LLM, Analytics, Database, Storage", "professionalSkills" : [ "Product sense", "Science vision", "Execution" ], "hardSkillsList" : [ "ML", "LLM", "GenAI models" ], "hardSkills" : [ { "name" : "ML", "level" : "intermediate" }, { "name" : "LLM", "level" : "intermediate" }, { "name" : "GenAI models", "level" : "intermediate" } ], "majorSkills" : "Gen AI, Applied Science, Product Development", "majorSkillsList" : [ "Gen AI", "Applied Science", "Product Development" ], "coreResponsibilities" : "Manage projects at all stages, Tackle challenging problems, Coordinate high profile projects, Ensure customer and business goals are met, Interface across product, science, and engineering teams, Drive mechanisms for team efficiency, Operate with high degree of autonomy, Prioritize projects and features, Plan projects and features, Manage partners, Track external commitments, Recommend alternative technical approaches, Partner with product, science, and engineering teams, Meet timelines, Role model use of GenAI, Design Gen AI solutions, Implement Gen AI solutions, Create effective ad campaigns, Improve overall experience", "teamCollaboration" : "Cross functional teams; Ads and Retail organizations", "projectManagementSkills" : [ "Project management", "Roadmap strategy", "Roadmap definition" ], "domainKnowledge" : "Generative AI; Advertising landscape; Brand advertising", "domainList" : [ "generative-ai", "advertising-landscape", "brand-advertising" ], "domainSpecificKnowledge" : [ "Generative AI", "Advertising landscape", "Brand advertising" ], "innovationCreativity" : "Invent new product experiences", "qualificationsBasic" : "PhD in Electrical Engineering, Computer Science, Mathematics, or related technical field, 7+ years working with Data & AI technologies, 7+ years designing or architecting systems, 7+ years working directly with engineering teams, 7+ years technical product or program management experience", "qualificationsPreferred" : "8+ years hands-on managing complex technology projects, Experience managing projects across cross functional teams", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Sponsored Brands team at Amazon is building the next generation of brand advertising products and platforms for advertisers using Gen AI. This role presents an opportunity to join us at the ground floor of this transformation, which is a core part of Amazon's overall business strategy. You will be the Gen AI applied science leader that will determine what products and ads show up in the most critical ad placements across Amazon. You'll have opportunities to deliver at the highest technical level, working with science, product, engineering, and UX, all directly connected to a marketplace of supply and demand. In this highly visible role, you'll collaborate across multiple stakeholders within Ads and Retail. We are seeking a Gen AI Applied Science inventor who has strong product sense, with a proven track record with hands on science vision and execution while building high impact products from the ground up. you will have the opportunity to apply your deep subject matter expertise in the area of ML, LLM and GenAI models. You will invent new product experiences that enable novel advertiser and shopper experiences. This role will work on bringing state-of-the-art GenAI models to production. You will define the long-term science vision for our advertising business, driven by our customer’s needs, and translate it into actionable plans for our team of applied scientists and engineers. Key job responsibilities You will play a pivotal role in managing projects at all stages: inception, design, development, deployment and subsequent improvements. You will tackle challenging problems and coordinate high profile projects across multiple teams to ensure customer and business goals are met. You will interface across product, science, and engineering teams within the advertising and retail organizations. You will drive mechanisms that allow the team to move quickly and deliver strong results. You will operate with a high degree of autonomy and efficiency. You'll be responsible for project management - prioritize, plan projects and features, manage partners, and track external commitments. You will recommend alternative technical approaches and partner with product, science, and engineering teams to meet timelines. You will actively role model the use of GenAI to make the team more efficient. About the team We are on a mission to make Amazon the best in class destination for shoppers to discover, engage, and purchase relevant products, from brands that are relevant to them. In this role, you will design and implement Gen AI solutions that help millions of advertisers create more effective ad campaigns with intelligent recommendations, while improving the overall experience at Amazon's global scale. Our team invents, defines, and delivers advertising products that drive brand discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon Store businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, fast-paced, and collaborative team with an entrepreneurial spirit. Basic Qualifications: - PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field - 7+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience - Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems - 7+ years of working directly with engineering teams experience - 7+ years of technical product or program management experience - Experience owning/driving roadmap strategy and definition Preferred Qualifications: - 8+ years of hands-on work managing complex technology projects experience - Experience managing projects across cross functional teams, building sustainable processes and coordinating release schedules Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, CA, Palo Alto - 228,700.00 - 309,400.00 USD annually USA, NY, New York - 218,800.00 - 295,900.00 USD annually USA, WA, SEATTLE - 198,900.00 - 269,000.00 USD annually", "summaryDescription" : "Principal Applied Scientist, Sponsored Products and Brands at Amazon.com Services LLC. Skills: Gen AI, Applied Science, Product Development. Manage projects at all stages. Tackle challenging problems", "postedDate" : "2026-06-13T15:11:57.473", "createdAt" : "2026-06-13T15:11:57.473", "lastUpdated" : "2026-06-15T06:34:06.169", "ingestTimestamp" : "2026-06-13T15:11:57.473", "mongoId" : "69fda4e34c40e2acd1b9974a", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.jobs/jobs/10413683/apply", "salaryAnnualMin" : 228700, "salaryAnnualMax" : 309400, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "ML", "LLM", "GenAI models", "Gen AI", "Applied Science", "Product Development", "Product sense", "Science vision", "Execution", "Project management", "Roadmap strategy", "Roadmap definition", "Generative AI", "Advertising landscape", "Brand advertising" ], "skillCount" : 15, "jobCategory" : "other", "dataQualityScore" : 1.0, "_score" : 701.5655, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Advertising" }, { "id" : "69164db070f0b99d586f3693", "title" : "Principal Applied Scientist, Artificial General Intelligence", "company" : "Amazon.com Services LLC", "industry" : "Machine Learning Science, Applied Science, Artificial Intelligence", "salary" : { "min" : 179000.0, "max" : 309400.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Sunnyvale, California, USA", "locations" : [ "Sunnyvale, California, USA" ], "timestamp" : "2026-06-15T00:12:36.857", "fingerprint" : "7dea8d2de937da682823d635f00681c3c662c9d5cf4a119167f1c46d5d163229", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3120830/apply", "externalId" : "707427f7-01f9-4573-b172-1cbe2f7b627c", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Sunnyvale", "state" : "California", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "37.3688301,-122.0363490" } ], "locationText" : "Sunnyvale, California, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, Java, C++, Artificial Intelligence, Natural Language Processing, Machine Learning, Generative AI", "professionalSkills" : [ "Algorithm design", "Model structure", "Scientific reviews", "Experimental design", "Research procedures", "Full-cycle development" ], "hardSkillsList" : [ "ML techniques", "Rigorous experiments", "Scalable science", "Engineering solutions" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "ML techniques", "level" : "intermediate" }, { "name" : "Rigorous experiments", "level" : "intermediate" }, { "name" : "Scalable science", "level" : "intermediate" }, { "name" : "Engineering solutions", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Artificial General Intelligence, Generative AI, Machine Learning", "majorSkillsList" : [ "Artificial General Intelligence", "Generative AI", "Machine Learning" ], "coreResponsibilities" : "Define research directions, Adopt new ML techniques, Invent new ML techniques, Conduct rigorous experiments, Publish research results, Translate research into practice, Develop long-term strategies, Persuade teams to adopt strategies, Propose goals, Deliver on goals, Participate in organizational planning, Participate in hiring, Participate in mentorship, Participate in leadership development, Build scalable science solutions, Build scalable engineering solutions, Serve as scientific resource, Lead scientific reviews, Scrutinize experimental design, Scrutinize modeling, Scrutinize verification, Scrutinize research procedures, Probe assumptions, Illuminate pitfalls, Foster shared understanding, Align teams toward strategies, Educate scientific community, Keep community updated, Guide career growth, Develop scientists, Develop leads, Drive development of GenAI, Handle Amazon-scale use cases, Impact customer experiences, Conceive solutions, Design solutions, Implement solutions, Test solutions, Document solutions, Deliver solutions, Maintain solutions", "teamCollaboration" : "Cross-functional projects; Organizational planning", "domainKnowledge" : "Artificial Intelligence; Natural Language Processing; Computational Cognitive Science; Generative AI", "domainList" : [ "artificial-intelligence", "natural-language-processing", "computational-cognitive-science", "generative-ai" ], "domainSpecificKnowledge" : [ "Artificial Intelligence", "Natural Language Processing", "Computational Cognitive Science", "Generative AI" ], "communicationSkills" : "Written communication; Spoken communication; Executive presentations", "documentationSkills" : "Documentation", "innovationCreativity" : "Invent new ML techniques", "problemSolvingSkills" : "Decompose complex problems; Troubleshoot issues", "outcomesObjectives" : "Impact customer experiences", "qualificationsBasic" : "5+ years ML experience, Graduate degree in AI, NLP, ML, or computational cognitive science, 7+ years academic and research experience, Publication record, Track record of project delivery, Programming in Java, C++, Python, Excellent communication skills", "qualificationsPreferred" : "PhD in AI, NLP, ML, or computational cognitive science, 10+ years academic and research experience, First author research, Functional thought leader, Experience influencing executive level, Experience mentoring research scientists, Distilling customer requirements, Excellent communication skills", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "As a Principal Scientist in the Artificial General Intelligence (AGI) organization, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modeling, verification and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. You will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will be technically exceptional with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). Basic Qualifications: - 5+ years of practical work applying ML to solve complex problems experience - Graduate degree in with specialization in artificial intelligence, natural language processing, machine learning, or computational cognitive science - 7+ years of combined academic and research experience - Strong publication record in top-tier journals and conferences - Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements - Experience programming in Java, C++, Python or related language - Excellent written and spoken communication skills Preferred Qualifications: - PhD with specialization in Artificial Intelligence, Natural Language Processing, Machine Learning, or Computational Cognitive Science - 10+ years of combined academic and research experience - First author research on scaling laws, evaluation metrics, data mixture optimization, data efficiency, or neural network architectures in top-tier journals and conferences - Functional thought leader and sought after for key tech decisions - Experience influencing decisions at an executive level - Experience mentoring and training the research scientist community on complex technical issues - Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives - Excellent written and spoken communication skills with successful delivery to customers Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.", "summaryDescription" : "Principal Applied Scientist, Artificial General Intelligence at Amazon.com Services LLC. Skills: Artificial General Intelligence, Generative AI, Machine Learning. Define research directions. Adopt new ML techniques", "postedDate" : "2026-06-13T00:20:37.410", "createdAt" : "2026-06-13T00:20:37.410", "lastUpdated" : "2026-06-15T00:12:36.857", "ingestTimestamp" : "2026-06-13T00:20:37.410", "mongoId" : "69164db070f0b99d586f3693", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3120830/apply", "salaryAnnualMin" : 179000, "salaryAnnualMax" : 309400, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "ML techniques", "Rigorous experiments", "Scalable science", "Engineering solutions", "Leadership", "Mentoring", "Artificial General Intelligence", "Generative AI", "Machine Learning", "Algorithm design", "Model structure", "Scientific reviews", "Experimental design", "Research procedures", "Full-cycle development", "Artificial Intelligence", "Natural Language Processing", "Computational Cognitive Science" ], "skillCount" : 18, "jobCategory" : "data", "dataQualityScore" : 1.0, "_score" : 699.0778, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "6a3050b7f292fd229cef44d0", "title" : "Principal Engineer", "company" : "Snowflake", "industry" : "Engineering", "salary" : { "min" : 250000.0, "max" : 380000.0, "currency" : "USD", "frequency" : "Yearly" }, "location" : "US-WA-Bellevue", "locations" : [ "US-WA-Bellevue" ], "timestamp" : "2026-06-16T10:45:51.416", "fingerprint" : "9e7f77f56c1870eee9a2a2eb7469e2c526846ec7a86a768244ce9304fd89397f", "source" : "ashby", "language" : "en", "applyUrl" : "https://jobs.ashbyhq.com/Snowflake/a265dbe7-bf24-4443-b54e-8da34ab5adad/application", "externalId" : "a265dbe7-bf24-4443-b54e-8da34ab5adad", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Bellevue", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.6144219,-122.1923370" } ], "locationText" : "Bellevue, Washington, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, Docker, Kubernetes, SPCS, JavaScript, TypeScript", "professionalSkills" : [ "Platform design", "Systems architect", "Organizational influence", "API design", "System architecture" ], "hardSkillsList" : [ "Container runtimes", "Server-side frameworks", "Distributed execution", "Performance tuning", "Reliability engineering", "Lifecycle management", "Multi-tenancy", "SaaS security" ], "softSkillsList" : [ "Leadership" ], "hardSkills" : [ { "name" : "Container runtimes", "level" : "intermediate" }, { "name" : "Server-side frameworks", "level" : "intermediate" }, { "name" : "Distributed execution", "level" : "intermediate" }, { "name" : "Performance tuning", "level" : "intermediate" }, { "name" : "Reliability engineering", "level" : "intermediate" }, { "name" : "Lifecycle management", "level" : "intermediate" }, { "name" : "Multi-tenancy", "level" : "intermediate" }, { "name" : "SaaS security", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" } ], "majorSkills" : "Platform engineering, Developer platforms, Containerization, Distributed systems", "majorSkillsList" : [ "Platform engineering", "Developer platforms", "Containerization", "Distributed systems" ], "coreResponsibilities" : "Define architectural vision for SiS platform, Own architectural vision for SiS platform, Drive platform evolution toward next-generation capabilities, Shape architectural direction for development areas, Serve as primary technical representative for SiS, Establish engineering standards for performance, Establish engineering standards for reliability, Establish engineering standards for scale, Lead technical design process, Lead review process for initiatives, Author specs, Implement changes, Review PRs, Set high bar for code quality, Set high bar for API design, Set high bar for system architecture, Mentor senior engineers, Grow senior engineers, Identify next generation technical leaders, Incubate next generation technical leaders, Level up team through example, Level up team through high standards, Level up team through knowledge sharing, Partner with Product, Partner with Design, Drive roadmap, Translate customer needs into solutions, Advocate for SiS developers, Advocate for SiS end users, Balance developer experience, Balance scalability needs", "teamCollaboration" : "Cross-team programs; Company-wide architecture forums; Cross-functional initiatives; Infrastructure teams; Snowsight teams; Cortex/AI teams; Security teams; Open-source Streamlit project; Product teams; Design teams", "projectManagementSkills" : [ "Roadmap planning" ], "domainKnowledge" : "Data platform; App development platform; Container runtimes; Embedding SDK; Developer experience; AI ecosystem; Data ecosystem; Web platform; Cross-origin security", "domainList" : [ "data-platform", "app-development-platform", "container-runtimes", "embedding-sdk", "developer-experience", "ai-ecosystem", "data-ecosystem", "web-platform", "cross-origin-security" ], "domainSpecificKnowledge" : [ "Data platform", "App development platform", "Container runtimes", "Embedding SDK", "Developer experience", "AI ecosystem", "Data ecosystem", "Web platform", "Cross-origin security" ], "toolProficiency" : [ "Docker", "Kubernetes" ], "communicationSkills" : "Technical representation", "documentationSkills" : "Authoring specs", "innovationCreativity" : "Experimental mindset; Test emerging capabilities", "problemSolvingSkills" : "Solve problems", "qualificationsBasic" : "15+ years software engineering experience, Architecting and shipping production-grade developer platforms, Expertise in Python application runtimes, Expertise in server-side frameworks, Expertise in distributed execution environments, Deep understanding of performance at scale, Deep understanding of reliability at scale, Deep understanding of lifecycle management at scale, Understanding of container-based execution environments, Hands-on experience designing SDKs, Hands-on experience designing embedding APIs, Hands-on experience designing component libraries, Demonstrated ability to lead technical programs, Experience with enterprise SaaS reliability requirements", "qualificationsPreferred" : "Familiarity with open-source Streamlit framework, Familiarity with Python-based app frameworks, Experience with IdP integrations, Experience with OAuth flows, Experience with authentication systems, Familiarity with web platform, Familiarity with TypeScript/JavaScript, Familiarity with browser APIs, Familiarity with WebSockets, Familiarity with service workers, Familiarity with iframe/cross-origin security models", "remoteFriendly" : true, "rawDescription" : "At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Streamlit in Snowflake (SiS) is our flagship app development platform, enabling data engineers, analysts, and developers to build and deploy interactive, data-driven applications directly within Snowflake. As Principal Engineer for the Streamlit in Snowflake team, you will be the top technical voice shaping how millions of users build and deploy apps on Snowflake's data platform. This role requires a rare combination: deep hands-on engineering expertise in Python/container runtimes, a systems architect's instinct for platform design, and the organizational influence to drive cross-team programs. You will define how SiS evolves from its container-native SPCS runtime and embedding/iframe SDK, to its developer experience, performance at scale, and integration with Snowflake's broader AI and data ecosystem. AS THE PRINCIPAL ENGINEER FOR STREAMLIT IN SNOWFLAKE YOU WILL: - Define and own the architectural vision for the SiS platform spanning the SPCS container runtime, the warehouse runtime, the embedding SDK, developer tooling, and the Snowsight integration layer. - Drive the platform's evolution toward its next-generation capabilities building on publicly shipping features like chromeless viewer URLs and IdP integration, and shaping the architectural direction for areas still in development. - Serve as the primary technical representative for SiS in company-wide architecture forums, shaping Snowflake's broader app platform strategy. - Establish engineering standards for performance, reliability, and scale ensuring SiS can handle many concurrent users with fast startup times and high availability. - Lead the technical design and review process for high-stakes, cross-functional initiatives: working closely with infrastructure, Snowsight, Cortex/AI, security, and the open-source Streamlit project. - Be a strong hands-on contributor by authoring specs, implementing changes, reviewing PRs, and setting a high bar for code quality, API design, and system architecture. - Mentor and grow senior engineers on the team; identify and incubate the next generation of technical leaders. Level up the team through your example and high standards and knowledge sharing. - Partner with Product and Design to drive the roadmap and translate customer needs (performance, scale, OSS parity) into robust engineering solutions. - Advocate for SiS developers and end users by balancing the developer experience of Python engineers with the scalability needs of enterprise customers embedding Streamlit. OUR IDEAL PRINCIPAL ENGINEER WILL HAVE: - 15+ years of software engineering experience, with a demonstrated track record of architecting and shipping production-grade developer platforms or application runtimes. - Strong expertise in Python application runtimes, server-side frameworks, and distributed execution environments with a deep understanding of performance, reliability, and lifecycle management at scale. - Strong understanding of container-based execution environments (Docker, Kubernetes, SPCS or similar) and the performance/reliability tradeoffs they introduce. - Hands-on experience designing and evolving developer-facing SDKs, embedding APIs, or component libraries including thoughtful API versioning and backward compatibility. - Demonstrated ability to lead technical programs that span multiple teams and organizational boundaries driving clarity, alignment, and accountability without direct authority. - Experience with enterprise SaaS reliability requirements: multi-tenancy, scale, security review processes, and graceful migrations. - Familiarity with the open-source Streamlit framework or similar Python-based app frameworks is a strong plus. - Experience working with IdP integrations, OAuth flows, or authentication systems for embedded/public-facing web applications is a plus. - (nice to have) Familiarity with the web platform (TypeScript/JavaScript, browser APIs, WebSockets, service workers, iframe/cross-origin security models) particularly as it relates to how Python-backed apps are served and embedded in the browser WHY JOIN THE STREAMLIT IN SNOWFLAKE TEAM AT SNOWFLAKE? Streamlit is one of the most loved developer tools in the data ecosystem and Streamlit in Snowflake is how we bring that experience to millions of enterprise users at scale. As a member of this team, you will: - Shape the future of data app development determining how the next generation of SiS runs, scales, and integrates with Snowflake's AI and data platform. - Solve genuinely hard problems at the intersection of distributed systems, web platform engineering, and developer experience from sub-second cold starts to embedding apps inside third-party products. - Work alongside the core Streamlit open-source contributors and influence the direction of one of the fastest-growing Python frameworks. - Join a tight-knit, high-ownership engineering team that has shipped SiS vNext from private preview to GA and is now building the features that will define the next chapter of the product. - Be part of a company that combines the resources and reach of a market leader with a decisive, ship-it-today engineering culture. Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com http://careers.snowflake.com", "summaryDescription" : "Principal Engineer at Snowflake. Skills: Platform engineering, Developer platforms, Containerization, Distributed systems. Define architectural vision for SiS platform. Own architectural vision for SiS platform", "createdAt" : "2026-06-16T00:26:40.534", "lastUpdated" : "2026-06-16T10:45:51.416", "ingestTimestamp" : "2026-06-16T00:26:40.534", "mongoId" : "6a3050b7f292fd229cef44d0", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 250000, "salaryAnnualMax" : 380000, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "Container runtimes", "Server-side frameworks", "Distributed execution", "Performance tuning", "Reliability engineering", "Lifecycle management", "Multi-tenancy", "SaaS security", "Leadership", "Platform engineering", "Developer platforms", "Containerization", "Distributed systems", "Platform design", "Systems architect", "Organizational influence", "API design", "System architecture", "Roadmap planning", "Data platform", "App development platform", "Embedding SDK", "Developer experience", "AI ecosystem", "Data ecosystem", "Web platform", "Cross-origin security", "Docker", "Kubernetes" ], "skillCount" : 29, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 0.8888889, "_score" : 686.21173, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "68e6b1d870f0b99d58d66a84", "title" : "Principal Applied Scientist, Hardware Silicon and Systems Group", "company" : "ADCI", "industry" : "Machine Learning Science, Applied Science, alexa and amazon devices", "salary" : { "min" : 6000000.0, "max" : 9000000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Principal Applied Scientist in Bengaluru, India, adjusted for executive seniority and technology industry." }, "location" : "Bengaluru, Karnataka, IND", "locations" : [ "Bengaluru, Karnataka, IND" ], "timestamp" : "2026-06-16T04:14:17.090", "fingerprint" : "de28bd31718a0a3a760c707970e79f984e16268c138d3264f4dc82a4f411e979", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3103322/apply", "externalId" : "601eb834-4d4c-46c8-a0b4-013265096258", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Bengaluru", "state" : "Karnataka", "country" : "India", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "12.9767936,77.5900820" } ], "locationText" : "Bengaluru, Karnataka, India", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Machine learning, Deep learning, Computer architecture, Inference algorithms, TensorFlow, PyTorch, ONNX", "professionalSkills" : [ "Model architecture design", "Model optimization", "Model deployment", "Hardware acceleration", "Efficient inference", "Model compression", "Quantization", "Pruning", "Distillation", "Multimodal optimization" ], "hardSkillsList" : [ "Hardware-aware quantization", "Low-precision arithmetic" ], "hardSkills" : [ { "name" : "Hardware-aware quantization", "level" : "intermediate" }, { "name" : "Low-precision arithmetic", "level" : "intermediate" } ], "majorSkills" : "ML models, Hardware optimization, Model compression, On-device ML", "majorSkillsList" : [ "ML models", "Hardware optimization", "Model compression", "On-device ML" ], "coreResponsibilities" : "Own technical architecture, Optimize ML models, Develop novel model architectures, Establish methodologies for model compression, Create evaluation framework, Implement multimodal optimization techniques, Define technical standards, Drive research initiatives, Develop novel ML architectures, Write critical optimization code, Create proof-of-concept implementations, Influence architecture decisions, Establish standards for model optimization, Mentor others", "teamCollaboration" : "Cross-functional teams", "domainKnowledge" : "On-device ML models; ML innovation; Silicon design; Resource-constrained devices; Consumer AI devices; Model training; Vision tasks; Language tasks; Multi-modal tasks; Custom silicon; Edge devices", "domainList" : [ "on-device-ml-models", "ml-innovation", "silicon-design", "resource-constrained-devices", "consumer-ai-devices", "model-training", "vision-tasks", "language-tasks", "multi-modal-tasks", "custom-silicon", "edge-devices" ], "domainSpecificKnowledge" : [ "On-device ML models", "ML innovation", "Silicon design", "Resource-constrained devices", "Consumer AI devices", "Model training", "Vision tasks", "Language tasks", "Multi-modal tasks", "Custom silicon", "Edge devices" ], "innovationCreativity" : "Industry-first model architectures; Breakthrough efficiency gains", "qualificationsBasic" : "Masters degree in Computer Science, 8+ years of experience in machine learning, Expertise in developing deep learning models, Background in computer architecture, Hands-on experience with model compression techniques, Proficiency with deep learning frameworks", "qualificationsPreferred" : "PhD in Computer Science, 10+ years of experience in machine learning, Proven expertise in co-designing ML models, In-depth understanding of model compression, Experience working on resource-constrained embedded systems, Demonstrated ability to influence technical strategy, Mentor cross-functional teams", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Hardware Silicon and Systems Group leads the development and optimization of on-device ML models for Amazon's hardware products, including audio, vision, and multi-modal AI features. We work at the critical intersection of ML innovation and silicon design, ensuring AI capabilities can run efficiently on resource-constrained devices. Currently, we enable production ML models across multiple device families, including Echo, Ring/Blink, and other consumer devices. Our work directly impacts Amazon's customer experiences in consumer AI device market. The solutions we develop determine which AI features can be offered on-device versus requiring cloud connectivity, ultimately shaping product capabilities and customer experience across Amazon's hardware portfolio. This is a unique opportunity to help shape the future of AI in consumer devices at unprecedented scale. You'll be at the forefront of developing industry-first model architectures and compression techniques that will power AI features across millions of Amazon devices worldwide. Your innovations will directly enable new AI features that enhance how customers interact with Amazon products every day. As Principal Applied Scientist you will blend expertise at the intersection of ML and hardware optimization for model training, build cutting-edge architectures for vision, language, and multi-modal tasks. Role requires a specialist in hardware-aware quantization, with hands-on experience in model compression techniques like pruning and distillation. You will be responsible for computer architecture, ML accelerator designs, efficient inference algorithms and low-precision arithmetic. Key job responsibilities As a Principal Applied Scientist, you will: • Own the technical architecture and optimization strategy for ML models deployed across Amazon's device ecosystem, from existing to yet-to-be-shipped products. • Develop novel model architectures optimized for our custom silicon, establishing new methodologies for model compression and quantization. • Create an evaluation framework for model efficiency and implement multimodal optimization techniques that work across vision, language, and audio tasks. • Define technical standards for model deployment and drive research initiatives in model efficiency to guide future silicon designs. • Spend the majority of your time doing deep technical work - developing novel ML architectures, writing critical optimization code, and creating proof-of-concept implementations that demonstrate breakthrough efficiency gains. • Influence architecture decisions impacting future silicon generations, establish standards for model optimization, and mentor others in advanced ML techniques. Basic Qualifications: Basic Qualifications: • Masters degree in Computer Science, Electrical Engineering, or a related technical field • 8+ years of experience in machine learning, with a focus on model architecture design, optimization, and deployment • Expertise in developing and deploying deep learning models for real-world applications, including vision, language, and multi modal tasks • Strong background in computer architecture, hardware acceleration, and efficient inference algorithms • Hands-on experience with model compression techniques such as pruning, quantization, and distillation • Proficiency with deep learning frameworks like TensorFlow, PyTorch, or ONNX Preferred Qualifications: • PhD in Computer Science, Electrical Engineering, or a related technical field • 10+ years of experience in machine learning, with a track record of developing novel model architectures and optimization techniques • Proven expertise in co-designing ML models and hardware accelerators for efficient on-device inference • In-depth understanding of the latest advancements in model compression, including techniques like knowledge distillation, network pruning, and hardware-aware quantization • Experience working on resource-constrained embedded systems and deploying ML models on edge devices • Demonstrated ability to influence technical strategy and mentor cross-functional teams • Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical stakeholders Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.", "summaryDescription" : "Principal Applied Scientist, Hardware Silicon and Systems Group at ADCI. Skills: ML models, Hardware optimization, Model compression, On-device ML. Own technical architecture. Optimize ML models", "postedDate" : "2026-06-14T19:48:18.334", "createdAt" : "2026-06-14T19:48:18.334", "lastUpdated" : "2026-06-16T04:14:17.090", "ingestTimestamp" : "2026-06-14T19:48:18.334", "mongoId" : "68e6b1d870f0b99d58d66a84", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3103322/apply", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Hardware-aware quantization", "Low-precision arithmetic", "ML models", "Hardware optimization", "Model compression", "On-device ML", "Model architecture design", "Model optimization", "Model deployment", "Hardware acceleration", "Efficient inference", "Quantization", "Pruning", "Distillation", "Multimodal optimization", "On-device ML models", "ML innovation", "Silicon design", "Resource-constrained devices", "Consumer AI devices", "Model training", "Vision tasks", "Language tasks", "Multi-modal tasks", "Custom silicon", "Edge devices" ], "skillCount" : 26, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 1.0, "_score" : 684.79224, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "69fe94ff4c40e2acd1121a1f", "title" : "Sr. Principal Scientist, Stores Economics and Science", "company" : "Amazon.com Services LLC", "industry" : "Applied Science, Technology, core ai", "salary" : { "min" : 240100.0, "max" : 324800.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Seattle, Washington, USA", "locations" : [ "Seattle, Washington, USA" ], "timestamp" : "2026-06-15T00:00:48.089", "fingerprint" : "552f8d04abbb98c0f713be8076a9748404c182c50620108c624992e4f150f7f1", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.jobs/jobs/10414402/apply", "externalId" : "49ad8287-3f40-435c-8e77-604d4421a595", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Seattle", "state" : "Washington", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "47.6038321,-122.3300620" } ], "locationText" : "Seattle, Washington, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "PhD", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "R, Python, Matlab, STATA", "professionalSkills" : [ "Economic analysis", "Data science", "Business analysis" ], "hardSkillsList" : [ "Statistical analysis", "Machine learning", "Econometrics" ], "hardSkills" : [ { "name" : "Statistical analysis", "level" : "intermediate" }, { "name" : "Machine learning", "level" : "intermediate" }, { "name" : "Econometrics", "level" : "intermediate" } ], "majorSkills" : "Machine learning, Economics, Data analysis", "majorSkillsList" : [ "Machine learning", "Economics", "Data analysis" ], "coreResponsibilities" : "Provide structure around complex business problems, Work with machine learning scientists to estimate and, Help business and tech partners turn analysis results, Own the development of scientific models, Manage data analysis, modeling, and experimentation, Estimate and validate models, Work with business partners to communicate analysis/modeling properties, Incorporate feedback and requests into projects", "teamCollaboration" : "Business and tech partners; Scientists and engineers; Business teams", "domainKnowledge" : "Stores economy; Buyer and seller economy; Econometrics", "domainList" : [ "stores-economy", "buyer-and-seller-economy", "econometrics" ], "domainSpecificKnowledge" : [ "Stores economy", "Buyer and seller economy", "Econometrics" ], "communicationSkills" : "Convey complicated process and systems", "innovationCreativity" : "Creative thinkers", "problemSolvingSkills" : "Complex business problems", "outcomesObjectives" : "Major impact on Amazon's business", "qualificationsBasic" : "PhD in Economics, Computer Science, Operations Research or highly related field, Experience in industry, consulting, government, or academic research, Coding ability in a scripting language such as R, Python, Matlab, or STATA", "qualificationsPreferred" : "Written and verbal communications skills, Considerably more and broader experience, Track record in performing high-quality analysis of data at scale", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "We are looking for a science leader who is able to provide structure around complex business problems, work with machine learning scientists to estimate and validate their models on large scale data, and who can help business and tech partners turn the results of their analysis into policies, programs, and actions that have a major impact on Amazon’s business. We are looking for creative thinkers who can combine a strong economic toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas. At the senior level, we expect you be able to own the development of scientific models and to manage, in close collaboration with scientists and engineers, the data analysis, modeling, and experimentation that is necessary for estimating and validating your model. You will need to work with our business partners to communicate the properties of your analysis/modeling and be able to work to incorporate their feedback and requests into your project. Experience in applied economic analysis is essential, and you should be familiar with modern tools for data science and business analysis. About the team The Stores Economics and Science (SEAS) organization uses economics, statistics, and machine learning to understand and design the complex economy of Amazon’s network of buyers and sellers. We are an interdisciplinary team, committed to use of cutting edge technology and leveraging the strengths of engineers and scientists to build solutions for some of the toughest business problems at Amazon. Our team is composed in equal parts of software engineers and scientists trained in diverse fields including ML, robotics, computer vision (CV), natural language processing (NLP), distributed systems and econometrics. Our mission is to drive adoption of AI science and technology across Amazon by partnering with business teams on science-heavy problems, incubating products with high AI research risk, reducing friction and manual effort with AI tooling, and raising the scientific bar across all teams at Amazon. Basic Qualifications: - PhD in Economics, Computer Science, Operations Research or highly related field (Physics, Statistics, Mathematics). - Experience in industry, consulting, government, or academic research. - Coding ability in a scripting language such as R, Python, Matlab, or STATA. Preferred Qualifications: - Written and verbal communications skills to convey complicated process and systems to business partners and senior leadership. - Applicants with considerably more and broader, experience are also strongly encouraged. - Strong track record in performing high-quality analysis of data at scale. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, CA, Mountain View - 276,100.00 - 350,000.00 USD annually USA, WA, Seattle - 240,100.00 - 324,800.00 USD annually", "summaryDescription" : "Sr. Principal Scientist, Stores Economics and Science at Amazon.com Services LLC. Skills: Machine learning, Economics, Data analysis. Provide structure around complex business problems. Work with machine learning scientists to estimate and", "postedDate" : "2026-06-13T06:47:02.767", "createdAt" : "2026-06-13T06:47:02.767", "lastUpdated" : "2026-06-15T00:00:48.089", "ingestTimestamp" : "2026-06-13T06:47:02.767", "mongoId" : "69fe94ff4c40e2acd1121a1f", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.jobs/jobs/10414402/apply", "salaryAnnualMin" : 240100, "salaryAnnualMax" : 324800, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "Statistical analysis", "Machine learning", "Econometrics", "Economics", "Data analysis", "Economic analysis", "Data science", "Business analysis", "Stores economy", "Buyer and seller economy" ], "skillCount" : 10, "jobCategory" : "other", "dataQualityScore" : 1.0, "_score" : 683.6687, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "6a2c7fe274d8ee00a0956bbc", "title" : "Sr. Principal Scientist, Analytical Development (Small Molecule)", "company" : "Antares Therapeutics", "industry" : "Pharma", "salary" : { "min" : 205000.0, "max" : 240000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Boston, Massachusetts, United States", "locations" : [ "Boston", "Massachusetts", "United States" ], "timestamp" : "2026-06-15T07:53:19.521", "fingerprint" : "9191e6d7037cb3a1055ea876c17ebe9b9442330771ffa2f0346876520baadd7b", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/antaresrx/jobs/6020291004", "externalId" : "6020291004", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Boston", "state" : "Massachusetts", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "42.3588336,-71.0578303" } ], "locationText" : "Boston, Massachusetts, United States", "employmentType" : "FULL_TIME", "minimumEducation" : "PhD", "experienceMinYears" : 15, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Analytical development, Small molecule, Drug substance, Drug product, Analytical method development, Analytical validation, Solid state characterization, Formulations", "professionalSkills" : [ "Technical leadership", "Strategic direction", "Scientific alignment", "Technical achievement", "Scientific community leadership", "Technical problem solving", "Innovative solutions", "Project planning", "Resource allocation", "External collaborations", "Internal collaborations", "Stakeholder management" ], "hardSkillsList" : [ "Analytical method development", "Method validation", "Solid state characterization", "Formulation development", "Regulatory submissions" ], "softSkillsList" : [ "Leadership", "Mentorship" ], "hardSkills" : [ { "name" : "Analytical method development", "level" : "intermediate" }, { "name" : "Method validation", "level" : "intermediate" }, { "name" : "Solid state characterization", "level" : "intermediate" }, { "name" : "Formulation development", "level" : "intermediate" }, { "name" : "Regulatory submissions", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentorship" } ], "majorSkills" : "Analytical Development, Small Molecule, Drug Substance, Drug Product", "majorSkillsList" : [ "Analytical Development", "Small Molecule", "Drug Substance", "Drug Product" ], "coreResponsibilities" : "Provide technical leadership, Provide strategic direction, Lead analytical efforts, Support drug substance development, Support drug product development, Ensure successful execution, Leverage internal capabilities, Leverage external partners, Drive alignment, Lead cross-functional teams, Contribute to cross-functional teams, Manage technical issues, Manage risks, Maintain scientific expertise, Advance technical expertise, Apply innovative approaches, Solve complex challenges, Provide oversight, Review analytical documentation, Support regulatory submissions, Establish project plans, Manage project timelines, Manage project budgets, Manage resource allocation, Build collaborations, Maintain collaborations, Manage CDMO partnerships, Manage technical stakeholders", "teamCollaboration" : "Cross-functional teams; External partners; CDMOs; Technical stakeholders", "projectManagementSkills" : [ "Project plans", "Timelines", "Budgets", "Resource allocation" ], "domainKnowledge" : "Small molecule programs; Drug substance; Drug product; Phase-appropriate development; Control strategies; Pre-development activities; Pre-formulation; Solid state characterization; Formulations for animal studies; Quality guidelines; Regulatory guidelines; Regulatory submissions", "domainList" : [ "small-molecule-programs", "drug-substance", "drug-product", "phase-appropriate-development", "control-strategies", "pre-development-activities", "pre-formulation", "solid-state-characterization", "formulations-for-animal-studies", "quality-guidelines", "regulatory-guidelines", "regulatory-submissions" ], "domainSpecificKnowledge" : [ "Small molecule programs", "Drug substance", "Drug product", "Phase-appropriate development", "Control strategies", "Pre-development activities", "Pre-formulation", "Solid state characterization", "Formulations for animal studies", "Quality guidelines", "Regulatory guidelines", "Regulatory submissions" ], "communicationSkills" : "Written communication; Verbal communication", "documentationSkills" : "Analytical development documentation; Regulatory submissions", "innovationCreativity" : "Innovative approaches; New technologies", "problemSolvingSkills" : "Technical problem-solving; Complex challenges", "qualificationsBasic" : "PhD in chemistry, 15+ years relevant experience, Analytical development experience, Biotech or pharmaceutical settings, Phase-appropriate small molecule analytical method development, Validation experience, Advanced analytical principles, Laboratory techniques for drug substance, Drug product techniques, Control strategies application, Pre-development activities experience, Pre-formulation experience, Solid state characterization, Formulations for animal studies, Quality and regulatory guidelines, Analytical sections of regulatory submissions, Lead in cross-functional matrix environments, Set clear goals, Manage tasks, resources, priorities, Implement new technologies, Innovative approaches within development, Develop and manage project plans, Manage timelines and budgets, Internal and external teams management, Build effective collaborations, CDMO collaborations, Technical leads collaborations, Project teams collaborations, Mentoring direct reports", "qualificationsPreferred" : "Experience mentoring direct reports", "workSiteFlexibility" : "Onsite", "rawDescription" : "Senior Principal Scientist, Analytical Development (Small Molecule) Salary Band: $205,000-$240,000 Location: Downtown Boston, MA Position Overview The Senior Principal Scientist will provide technical leadership of analytical development within the CMC function across the Antares portfolio. Analytical Development is responsible for analytical activities spanning late-stage discovery (lead optimization and candidate nomination) through early clinical development and late-stage commercial readiness for small molecule programs. This individual will ensure success across drug substance and drug product deliverables by effectively leveraging internal and external resources, while driving strategic and scientific alignment of analytical development activities with overall program needs. The ideal candidate brings a strong track record of leadership, mentorship, and technical achievement within matrixed environments, and will act as a recognized leader in the scientific community—capable of resolving complex technical challenges while advancing innovative solutions across programs. Key Responsibilities Provide technical leadership and strategic direction for analytical development activities across programs Lead analytical efforts supporting drug substance and drug product development from discovery through commercial readiness Ensure successful execution of analytical deliverables by leveraging internal capabilities and external partners (e.g., CDMOs/CROs) Drive alignment between analytical development strategies and overall program objectives Lead and contribute to cross-functional teams, effectively managing technical issues and risks within a matrix environment Maintain and advance state-of-the-art scientific and technical expertise, applying innovative approaches to solve complex challenges Provide oversight and review of analytical development documentation to support regulatory submissions Establish and manage project plans, timelines, budgets, and resource allocation for analytical activities Build and maintain strong internal and external collaborations, including partnerships with CDMOs and technical stakeholders Qualifications PhD in chemistry, chemical technologies, or related field with 15+ years of relevant industry experience in analytical development within biotech or pharmaceutical settings Demonstrated expertise in phase-appropriate small molecule analytical method development and validation Extensive knowledge of advanced analytical principles and laboratory techniques for drug substance and drug product, including application of control strategies across all stages of development Experience with pre-development activities, including pre-formulation, solid state characterization, and formulations for animal studies Strong understanding of quality and regulatory guidelines, with experience supporting analytical sections of regulatory submissions Proven ability to lead in cross-functional matrix environments, set clear goals, and manage tasks, resources, and priorities Excellent written and verbal communication skills, with strong organizational, planning, and technical problem-solving capabilities Demonstrated ability to implement new technologies and innovative approaches within development programs Experience developing and managing project plans, timelines, and budgets across internal and external teams Proven track record of building effective collaborations with CDMOs, technical leads, and project teams Prior experience mentoring and potentially managing direct reports The anticipated annual salary range for this position is $205,000-$240,000 annually. The actual salary offered will be based on a number of factors, including but not limited to the qualifications of the applicant, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location in which the applicant lives and/or from which they will be performing the job.", "summaryDescription" : "Sr. Principal Scientist, Analytical Development (Small Molecule) at Antares Therapeutics. Skills: Analytical Development, Small Molecule, Drug Substance, Drug Product. Provide technical leadership. Provide strategic direction", "postedDate" : "2026-06-13T20:36:53.857", "createdAt" : "2026-06-13T20:36:53.857", "lastUpdated" : "2026-06-15T07:53:19.521", "ingestTimestamp" : "2026-06-13T20:36:53.857", "mongoId" : "6a2c7fe274d8ee00a0956bbc", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 205000, "salaryAnnualMax" : 240000, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "Analytical method development", "Method validation", "Solid state characterization", "Formulation development", "Regulatory submissions", "Leadership", "Mentorship", "Analytical Development", "Small Molecule", "Drug Substance", "Drug Product", "Technical leadership", "Strategic direction", "Scientific alignment", "Technical achievement", "Scientific community leadership", "Technical problem solving", "Innovative solutions", "Project planning", "Resource allocation", "External collaborations", "Internal collaborations", "Stakeholder management", "Project plans", "Timelines", "Budgets", "Small molecule programs", "Drug substance", "Drug product", "Phase-appropriate development", "Control strategies", "Pre-development activities", "Pre-formulation", "Formulations for animal studies", "Quality guidelines", "Regulatory guidelines" ], "skillCount" : 36, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 1.0, "_score" : 682.46454, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Biotech", "llmIndustryConfidence" : 0.95 }, { "id" : "6a2aa59674d8ee00a06b59ef", "title" : "Principal Data Scientist - Agent Builder", "company" : "Elastic", "industry" : "Technology", "salary" : { "min" : 140000.0, "max" : 210000.0, "currency" : "GBP", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "The estimated salary is based on the market baseline for a Principal Data Scientist in the Technology industry in the United Kingdom, with adjustments for executive seniority and company size." }, "location" : "United Kingdom", "locations" : [ "United Kingdom" ], "timestamp" : "2026-06-14T22:14:33.989", "fingerprint" : "d6a33fc94bf0dc8857ef6f299d2c7327c50f0478b13eaf6cf26eb536b4df0044", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/referralsuseonly/jobs/7988143", "externalId" : "7988143", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United Kingdom", "continent" : "eu", "workMode" : "onsite" } ], "locationText" : "United Kingdom", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, PyTorch, Transformers, Pandas, Elasticsearch, ES|QL, RAG, Vector search, NLP, IR", "professionalSkills" : [ "Technical strategy", "Product strategy", "Experiment design", "Evaluation methodology", "Statistical rigor", "LLM systems" ], "hardSkillsList" : [ "Semantic search", "Ranking", "Retrieval systems", "Model comparison", "Offline metrics", "Online experimentation", "LLM-as-judge", "Groundedness", "Citation quality", "Sparse retrieval", "Dense retrieval", "Query understanding", "Context enrichment", "Re-ranking approaches", "Cost/latency trade-offs" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Semantic search", "level" : "intermediate" }, { "name" : "Ranking", "level" : "intermediate" }, { "name" : "Retrieval systems", "level" : "intermediate" }, { "name" : "Model comparison", "level" : "intermediate" }, { "name" : "Offline metrics", "level" : "intermediate" }, { "name" : "Online experimentation", "level" : "intermediate" }, { "name" : "LLM-as-judge", "level" : "intermediate" }, { "name" : "Groundedness", "level" : "intermediate" }, { "name" : "Citation quality", "level" : "intermediate" }, { "name" : "Sparse retrieval", "level" : "intermediate" }, { "name" : "Dense retrieval", "level" : "intermediate" }, { "name" : "Query understanding", "level" : "intermediate" }, { "name" : "Context enrichment", "level" : "intermediate" }, { "name" : "Re-ranking approaches", "level" : "intermediate" }, { "name" : "Cost/latency trade-offs", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Data Science, Machine Learning, AI, LLM", "majorSkillsList" : [ "Data Science", "Machine Learning", "AI", "LLM" ], "coreResponsibilities" : "Define evaluation strategy, Lead design of quality metrics, Build retrieval approaches, Compare retrieval approaches, Guide improvements across retrieval, Turn experimental results into decisions, Partner with engineering to productionize pipelines, Influence roadmap, Mentor data scientists, Mentor engineers, Share outcomes through docs, Share outcomes through notebooks, Share outcomes through PRs, Share outcomes through dashboards, Share outcomes through proposals, Share outcomes through reviews", "teamCollaboration" : "Backend engineering; Product; UX; Data scientists; Engineering teams; Leadership audiences", "domainKnowledge" : "Conversational AI; Agentic platform; RAG; Vector search; Search AI; Distributed data systems", "domainList" : [ "conversational-ai", "agentic-platform", "rag", "vector-search", "search-ai", "distributed-data-systems" ], "domainSpecificKnowledge" : [ "Conversational AI", "Agentic platform", "RAG", "Vector search", "Search AI", "Distributed data systems" ], "toolProficiency" : [ "PyTorch", "Transformers", "Pandas", "Notebooks", "Elasticsearch" ], "communicationSkills" : "Explain complex trade-offs", "documentationSkills" : "Clear docs; Technical proposals", "innovationCreativity" : "Frontier problems; Prototype", "problemSolvingSkills" : "Ambiguous problems; Cutting-edge problems; Ambiguous domains; Emerging domains", "outcomesObjectives" : "Improve chat quality; Measurable product improvements; Ship improvements customers can feel", "qualificationsBasic" : "8+ years of applied DS/ML experience, Deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences, Track record defining and leading evaluation for production AI/ML systems, Hands-on ability with Python, Hands-on ability with PyTorch/Transformers, Hands-on ability with Pandas, Hands-on ability with notebooks, Hands-on ability with reproducible experiments, Hands-on ability with versioned datasets, Hands-on ability with clean, reviewable code, Understanding of retrieval systems, Experience collaborating closely with engineering teams, Practical Elasticsearch experience, Excellent written communication, Excellent verbal communication", "qualificationsPreferred" : "PhD preferred, Specific ML framework experience, Cloud platform certs, ES|QL familiarity", "workSiteFlexibility" : "Flexible", "rawDescription" : "Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Search Conversational Experiences team builds Elastic’s new conversational and agentic platform that lets customers chat with their own data in Elasticsearch. We build the core quality layer for RAG, agents and tools, retrieval and citations, streaming, memory, and the evaluation signals that turn open-ended questions into grounded, reliable answers. As a Principal Data Scientist, you will help set the technical direction for how we evaluate, improve, and scale chat quality across Elastic’s agentic platform. You will define the evaluation strategy that guides product decisions, including which models we standardize on, how we route requests across agents, which tools we enable and when, and how we tailor agents to different Elastic use cases in search and beyond. You will work closely with backend engineering, product, UX, and other data scientists to turn ambiguous, cutting-edge problems into measurable product improvements. You’ll help lead work on frontier problems such as folding RAG and vector search into an agent’s knowledge base, dynamically enriching model context to improve groundedness, shaping reasoning strategies and tool-selection policies, lighting up agent-driven visualizations on top of Elasticsearch data, and exploring multimodality where it can create meaningful user value. This is an applied leadership role: you will prototype, evaluate, influence roadmap direction, and help teams ship improvements that customers can feel. What You Will Be Doing Define the evaluation strategy for conversational and agentic search, including offline and online evaluation, golden datasets, rubrics, LLM-as-judge calibration, groundedness and citation checks, and A/B testing. Lead the design of quality metrics and decision frameworks for RAG, agents, tools, model selection, agent routing, prompt behavior, and cost/latency trade-offs. Build, compare, and guide improvements across retrieval and re-ranking approaches, including sparse and dense retrieval, vector search, query understanding, semantic rewrites, and context enrichment. Turn experimental results into product and business decisions: which models to use, how to route requests efficiently, which tools should be exposed, and how agents should be customized for different Elastic use cases. Partner with engineering to productionize evaluation pipelines, telemetry, dashboards, CI guardrails, and regression detection for chat quality, helpfulness, dedication, latency, and cost. Influence the roadmap by identifying the highest-leverage quality gaps, proposing practical solutions, and communicating trade-offs clearly to product, engineering, and leadership. Mentor other data scientists and engineers in experiment design, evaluation methodology, statistical rigor, and practical approaches to improving LLM-powered systems. Share outcomes through clear docs, notebooks, PRs, dashboards, technical proposals, and cross-functional reviews. What You Bring 8+ years of applied DS/ML experience, with deep expertise in IR, NLP, ranking, semantic search, RAG, or LLM-powered product experiences. Strong track record defining and leading evaluation for production AI/ML systems, including offline metrics, online experimentation, LLM-as-judge approaches, groundedness, citation quality, and model comparison. Experience influencing product and technical strategy through data, especially in ambiguous or emerging domains where the “right” metric or approach is not obvious at the start. Hands-on ability with Python, PyTorch/Transformers, Pandas, notebooks, reproducible experiments, versioned datasets, and clean, reviewable code. Strong understanding of retrieval systems, including dense and sparse retrieval, re-ranking, vector search, query understanding, and evaluation metrics such as nDCG, MRR, Recall@k, precision,and latency/cost trade-offs. Experience collaborating closely with engineering teams to move from prototype to production, including telemetry design, dashboards, CI guardrails, and quality regression tracking. Practical Elasticsearch experience, or experience with similar search and distributed data systems. ES|QL familiarity is a plus. Excellent written and verbal communication, with the ability to explain complex scientific and technical trade-offs to engineering, product, design, and leadership audiences. A collaborative, low-ego style and a strong ability to mentor, raise standards, and develop transparency for others in a distributed team. Additional Information - We Take Care of Our People As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. Competitive pay based on the work you do here and not your previous salary Health coverage for you and your family in many locations Ability to craft your calendar with flexible locations and schedules for many roles Generous number of vacation days each year Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service Up to 40 hours each year to use toward volunteer projects you love Embracing parenthood with minimum of 16 weeks of parental leave Different people approach problems differently. We need that. Elastic is an equal opportunity/affirmative action employer committed to diversity, equity, and inclusion. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation. We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co We will reply to your request within 24 business hours of submission. Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster) Elasticsearch develops and distributes encryption software and technology that is subject to U. S. export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Russia, Syria, the Crimea Region of Ukraine, the Donetsk People’s Republic (“DNR”), and the Luhansk People’s Republic (“LNR”). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic. Please see here for our Privacy Statement.", "summaryDescription" : "Principal Data Scientist - Agent Builder at Elastic. Skills: Data Science, Machine Learning, AI, LLM. Define evaluation strategy. 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Waltham, Massachusetts, United States", "minimumEducation" : "Bachelor's", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, PyTorch, TensorFlow, pandas, numpy, Generative AI, Large Language Models, LLMs, Transformers, GPT, VLMs", "professionalSkills" : [ "Technical leadership", "Strategic planning", "Roadmap development", "Business value delivery", "Scientific direction" ], "hardSkillsList" : [ "Machine learning", "Statistical methodology", "Multimodal document understanding", "Structured data extraction", "Text processing", "Image processing", "Generative modeling", "Scientific computing" ], "softSkillsList" : [ "Leadership" ], "hardSkills" : [ { "name" : "Machine learning", "level" : "intermediate" }, { "name" : "Statistical methodology", "level" : "intermediate" }, { "name" : "Multimodal document understanding", "level" : "intermediate" }, { "name" : "Structured data extraction", "level" : "intermediate" }, { "name" : "Text processing", "level" : "intermediate" }, { "name" : "Image processing", "level" : "intermediate" }, { "name" : "Generative modeling", "level" : "intermediate" }, { "name" : "Scientific computing", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" } ], "majorSkills" : "Generative AI, Machine learning, Large Language Models, Data extraction", "majorSkillsList" : [ "Generative AI", "Machine learning", "Large Language Models", "Data extraction" ], "coreResponsibilities" : "Own and drive a technical roadmap, Lead strategic planning, Lead roadmap development, Develop generative AI models, Deploy generative AI models, Develop large language models, Deploy large language models, Extract structured data, Lead exploration of data processing solutions, Lead development of data processing solutions, Train generative models, Fine-tune generative models, Train language models, Fine-tune language models", "teamCollaboration" : "Cross-functional ML initiatives; Cross-functional AI initiatives", "projectManagementSkills" : [ "Roadmap development" ], "domainKnowledge" : "Multimodal document processing; Technical drawings; Purchasing orders; Generative modeling", "domainList" : [ "multimodal-document-processing", "technical-drawings", "purchasing-orders", "generative-modeling" ], "domainSpecificKnowledge" : [ "Multimodal document processing", "Technical drawings", "Purchasing orders", "Generative modeling" ], "toolProficiency" : [ "PyTorch", "TensorFlow", "pandas", "numpy" ], "innovationCreativity" : "Innovation", "outcomesObjectives" : "Enhance Xometry's service offerings", "qualificationsBasic" : "Bachelor's degree required, 6+ years of relevant experience, Expert-level proficiency in Python, Proficient in Python, Proficient in PyTorch, Proficient in TensorFlow, Proficient in pandas, Proficient in numpy, Recognized depth in machine learning, Recognized depth in statistical methodology, Expertise in large-scale language models, Expertise in vision models, Experience with multimodal data processing, background in probability, background in statistics, background in optimization techniques", "qualificationsPreferred" : "Advanced degree (M. S. or PhD) preferred, PhD preferred, Experience with Transformers, Experience with GPT, Experience with VLMs, Experience combining text, image, and 3D data", "remoteFriendly" : true, "workSiteFlexibility" : "Hybrid", "rawDescription" : "Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity. About the Role Xometry is seeking an exceptional Principal Data Scientist to join our Generative AI team. The ideal candidate will have a passion for advancing machine learning and generative AI capabilities, particularly for fine-tuning generative and language models, multimodal document understanding, and structured data extraction. This role will also serve as a key technical leader for high-stakes strategic initiatives, including the DFM AI + IQE integration with Siemens. In this role, you will serve as a technical leader, owning a 12 month technical roadmap, driving large-scale cross-functional ML and AI initiatives, and setting the long-term scientific direction for AI-driven solutions that enhance Xometry's service offerings. This role requires a hybrid schedule (3 days a week) at our Waltham, MA or North Bethesda, MD office location. How You'll Contribute Technical Vision and Roadmap: Own and drive an 18-24 month technical roadmap that balances innovation with disciplined business value delivery. Lead strategic planning and roadmap development for generative AI initiatives, identifying high-impact projects and aligning them with Xometry’s business objectives. Generative AI Development: Develop and deploy generative AI models and large language models (LLMs) for multimodal document processing, focusing on extracting structured data from technical drawings, purchasing orders, and other complex documents. Lead the exploration and development of innovative text and image-based data processing solutions, including training and fine-tuning generative and language models. Technical Leadership or a PhD with 6-plus years of relevant experience. A bachelor’s degree is required, but an advanced degree (M. S. or PhD) in computer science, machine learning, AI, or a related field is highly preferred. Technical Mastery: Expert-level proficiency in Python and advanced scientific computing, with a track record of building large-scale, production-grade ML systems. Proficient in Python, including key libraries such as PyTorch, TensorFlow, pandas, and numpy. AI/ML Expertise: Recognized depth in machine learning and statistical methodology. Expertise in large-scale language and vision models (e.g., Transformers, GPT, VLMs) and experience with multimodal data processing (e.g., combining text, image, and 3D data). Strong background in probability, statistics, and optimization techniques relevant to generative modeling. Strategic life and disability insurance; generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave; EAP, other wellbeing resources; and much more. #LI-Hybrid Xometry is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status. For US based roles: Xometry participates in E-Verify and after a job offer is accepted, will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.", "summaryDescription" : "Principal Data Scientist - Gen AI at Xometry. Skills: Generative AI, Machine learning, Large Language Models, Data extraction. Own and drive a technical roadmap. 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Cross-functional collaborators; Cross-functional project teams; Venture teams; External partners", "projectManagementSkills" : [ "Program Leadership", "Strategic Plans" ], "domainKnowledge" : "Biomolecules; Drug Design; Molecular Modeling; Systems Biology; Protein Design; Mass Spectrometry; Cheminformatics; Biophysics; Agentic Automation", "domainList" : [ "biomolecules", "drug-design", "molecular-modeling", "systems-biology", "protein-design", "mass-spectrometry", "cheminformatics", "biophysics", "agentic-automation" ], "domainSpecificKnowledge" : [ "Biomolecules", "Drug Design", "Molecular Modeling", "Systems Biology", "Protein Design", "Mass Spectrometry", "Cheminformatics", "Biophysics", "Agentic Automation" ], "toolProficiency" : [ "DVC", "LakeFS", "MLflow", "gnina", "DiffDock", "Airflow", "Prefect", "Argo", "FAISS", "pgvector", "FastAPI", "Streamlit", "Gradio" ], "communicationSkills" : "Present complex findings", "continuousLearning" : "Stretch across domains; Present at conferences", "innovationCreativity" : "Sustained independent thought; Creativity; Propose new development strategies; Identify opportunities", "problemSolvingSkills" : "Deep Diver; Risk Assessment; Tradeoff Analysis", "outcomesObjectives" : "Create a portfolio of AI-first companies; Transform breakthrough ideas; Accelerate fundamental research", "qualificationsBasic" : "5+ years industry AI/ML experience, Experience driving results through teams, Sustained independent thought and creativity, Leadership and contribution to decision making, Depth across modern ML frameworks, Depth across version databases, Depth across deep learning, Depth across relevant informatics software", "qualificationsPreferred" : "Breadth across protein modeling/design, Breadth across proteomics/mass spec, Breadth across cheminformatics/docking/ADMET, Breadth across biophysics/MD, Breadth across LLM/agentic automation, MLOps expertise, Experience mentoring early hires, Experience acting as interim Head of ML, Experience contributing to hiring plans, Experience contributing to interview processes", "rawDescription" : "ABOUT PIONEERING INTELLIGENCE Pioneering Intelligence builds on Flagship Pioneering’s legacy of founding cutting-edge science and computational ventures, harnessing recent advances in AI, machine learning, and data to accelerate fundamental research and create a portfolio of AI-first companies. As part of Flagship’s integrated model of science, entrepreneurship, and capital, it transforms breakthrough ideas into world-changing companies, elevating the AI advances happening across the ecosystem in human health, sustainability, and beyond. THE ROLE We are seeking a Principal Scientist (Embedded ML/Computational) to lead multiple AI/ML or computational projects across early stage ventures, as a part of Flagship’s company origination process. You will define and deliver pragmatic AI strategies, oversee method and platform development (e.g., systems design, drug design, molecular modeling, systems biology, protein design, LLM/agentic workflows), and ensure rigor in model development, benchmarking, scaling, and reporting. You will manage cross functional contributors as applicable, influence company direction, and represent PI to venture teams and external partners. The ideal candidate is a self-directed serial deep diver - someone who can move from protein design one week to mass spec or docking pipelines the next and then spin up LLM based agents that automate scientific workflows. KEY RESPONSIBILITIES Program Leadership: Lead development, implementation, control, and reporting of several AI/ML or computational projects within assigned ventures in line with broader strategic plans of PI and Flagship, budgets, and timelines. Technical Ownership: Take a specialized technical role on project teams to oversee method development, pipeline development, and LLM based agent/workflow design; drive benchmarking, scaling, and implementation into production grade systems. Best Practices: promote operational excellence in AI projects by educating cross-functional collaborators. Team Leadership: Manage and/or coordinate internal and external scientists/engineers and crossfunctional project teams as applicable; mentor early hires; support recruiting and interview. Planning surface risks and tradeoffs early with clear options. Landscape synthesize concepts to propose new development strategies and identify opportunities for PI and venture portfolios. Representation act as a recognized subject matter expert; actively participate in scientific conferences and meetings. Communication adapt and present complex findings to diverse audiences to support meaningful interpretation and action. PROFESSIONAL EXPERIENCE industry AI/ML experience preferred. Experience driving results directly or indirectly through teams of engineers/scientists in dynamic, fastpaced, entrepreneurial, and technical environments. Clear evidence of sustained independent thought and creativity driving high impact, cross disciplinary AI/ML projects. Successful track record of leadership and contribution to decision making on progression of AI/ML models within projects or programs. Depth across multiple core tools and concepts, including Python; modern ML frameworks (PyTorch or JAX/TensorFlow); version control; databases; deep learning architectures; and relevant informatics software. Consistent record of outstanding performance reflected in publications, patents, or high impact internal reports where applicable Preferred Qualifications Breadth across domains such as protein modeling/design, proteomics/mass spec, cheminformatics/docking/ADMET, biophysics/MD, and LLM/agentic automation. MLOps expertise: data contracts and lineage (e.g., DVC/LakeFS), experiment tracking (MLflow/W docking rescoring (e.g., gnina, DiffDock) and pose quality metrics. Workflow orchestration (Airflow/Prefect/Argo), data warehouses (Redshift/Snowflake), vector search (FAISS/pgvector), and lightweight internal tools (FastAPI, Streamlit/Gradio). Experience mentoring early hires, acting as interim Head of ML, and contributing to hiring plans and interview processes at startups. WHY PIONEERING INTELLIGENCE Operate at the frontier: Build and deploy AI/ML that directly powers discovery and decision making across multiple new ventures. Own the full stack: From scoping and data contracts to models, MLOps, and internal UIs—ship endtoend systems that scientists rely on daily. Compound impact: Convert one off wins into shared libraries and templates adopted across the PI portfolio. Work with founders: Partner closely with venture leadership and PI platform engineers; influence strategy through hands on delivery. Grow fast: Stretch across domains, take on situational leadership, present at conferences, and help shape first ML teams. ABOUT FLAGSHIP PIONEERING: Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture. Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com. At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact. We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship. Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto. #LI-MB1 The salary range for this role is $208,000 - $286,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Pioneering Intelligence currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Pioneering Intelligence’s good faith estimate as of the date of publication and may be modified in the future. Privacy Notice for Applicants: When you apply for a role at Flagship Pioneering or one of its portfolio companies, we collect and use personal information you provide (such as your name, contact details, work history, and application materials) to evaluate your application, communicate with you, and comply with legal obligations. Your application data is processed through Greenhouse, our applicant tracking system, and may also be reviewed using AI-assisted screening tools. We do not sell your personal information. California residents have rights under the CCPA/CPRA including to know, delete, and opt out of the sharing of their personal information. If you are located in the EU or UK, we process your data under GDPR and you have rights to access, rectify, and erase your data. To exercise your rights or for questions, contact privacy@flagshippioneering.com.", "summaryDescription" : "Principal Scientist, Machine Learning - Biomolecules at Flagship Pioneering, Inc.. Skills: Machine Learning, Computational Projects, LLM Agents, Scientific Workflows. Lead development of AI/ML projects. Implement AI/ML projects", "postedDate" : "2026-06-11T21:27:46.546", "createdAt" : "2026-06-11T21:27:46.546", "lastUpdated" : "2026-06-14T22:16:25.313", "ingestTimestamp" : "2026-06-11T21:27:46.546", "mongoId" : "6a2a737c74d8ee00a064fdf5", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 208000, "salaryAnnualMax" : 286000, "salaryBand" : "200k+", "hasSalary" : true, "allSkills" : [ "Computational Projects", "AI Strategy", "Model Development", "Data Contracts", "Experiment Tracking", "Pose Quality Metrics", "Workflow Orchestration", "Vector Search", "Leadership", "Mentoring", "Machine Learning", "LLM Agents", "Scientific Workflows", "Program Leadership", "Technical Ownership", "Method Development", "Pipeline Development", "LLM Agent Workflow", "Benchmarking", "Scaling", "Systems Design", "Drug Design", "Molecular Modeling", "Systems Biology", "Protein Design", "Venture Portfolios", "Company Origination", "Strategic Plans", "Biomolecules", "Mass Spectrometry", "Cheminformatics", "Biophysics", "Agentic Automation", "DVC", "LakeFS", "MLflow", "gnina", "DiffDock", "Airflow", "Prefect", "Argo", "FAISS", "pgvector", "FastAPI", "Streamlit", "Gradio" ], "skillCount" : 46, "jobCategory" : "other", "jobSubcategory" : "artificial-intelligence", "dataQualityScore" : 0.8888889, "_score" : 679.9941, "daysOld" : 3, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Biotechnology" }, { "id" : "68ed10ae70f0b99d58e7bcd1", "title" : "Principal Applied Scientist, Prime Video Personalization & Discovery", "company" : "Amazon.com Services LLC", "industry" : "Machine Learning Science, Applied Science, entertainment", "salary" : { "min" : 179000.0, "max" : 309400.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Sunnyvale, California, USA", "locations" : [ "Sunnyvale, California, USA" ], "timestamp" : "2026-06-15T11:06:10.456", "fingerprint" : "635dfd8d7b9bc449f976ee094597d80695eac92026ecc70e2df38bea74c371c5", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3104623/apply", "externalId" : "b7f3a3b1-37d3-4118-a181-ca91ab249c9f", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Sunnyvale", "state" : "California", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "37.3688301,-122.0363490" } ], "locationText" : "Sunnyvale, California, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, Java, C++, TensorFlow, PyTorch, Deep learning, Generative AI, Reinforcement learning, Optimization", "professionalSkills" : [ "System design", "MLOps" ], "hardSkillsList" : [ "Recommendation systems", "Search relevance", "Personalization systems", "A/B testing", "Data analysis" ], "softSkillsList" : [ "Leadership", "Mentoring" ], "hardSkills" : [ { "name" : "Recommendation systems", "level" : "intermediate" }, { "name" : "Search relevance", "level" : "intermediate" }, { "name" : "Personalization systems", "level" : "intermediate" }, { "name" : "A/B testing", "level" : "intermediate" }, { "name" : "Data analysis", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Mentoring" } ], "majorSkills" : "Machine learning, Recommendation systems, Generative AI, Personalization", "majorSkillsList" : [ "Machine learning", "Recommendation systems", "Generative AI", "Personalization" ], "coreResponsibilities" : "Invent, prototype, and productionize AI solutions, Provide technical leadership, Influence product vision, Design and lead A/B tests, Validate hypotheses, Guide product decisions, Drive technical bar-raising, Mentor scientists, Review designs, Publish research internally and externally", "teamCollaboration" : "Collaborate with engineers; Collaborate with product managers; Collaborate with senior leaders", "domainKnowledge" : "Personalization systems; Discovery ecosystem; Recommendation systems; Generative AI", "domainList" : [ "personalization-systems", "discovery-ecosystem", "recommendation-systems", "generative-ai" ], "domainSpecificKnowledge" : [ "Personalization systems", "Discovery ecosystem", "Recommendation systems", "Generative AI" ], "communicationSkills" : "Technical leadership; Publish research", "continuousLearning" : "Stay ahead of industry trends", "innovationCreativity" : "Invent groundbreaking AI solutions; Push boundaries", "problemSolvingSkills" : "Data analysis", "outcomesObjectives" : "Deliver AI-powered customer experiences; Drive business goals", "qualificationsBasic" : "PhD or Master's with 10+ years experience, 8+ years ML model experience, Proven leadership in ML/AI initiatives, Proficiency in Python, Java, or C++, Experience with ML frameworks", "qualificationsPreferred" : "Strong publication record, PhD preferred", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Are you interested in shaping the future of entertainment through cutting-edge AI? Prime Video’s technology teams are redefining the digital video experience at scale. As a Principal Applied Scientist at Prime Video, you will be a technical and strategic leader responsible for inventing, developing, and deploying groundbreaking AI solutions that power personalized, relevant, and delightful experiences for millions of global customers. You will help shape the vision and direction of key ML systems that support Prime Video’s mission to deliver AI-powered customer experiences. This role demands a unique blend of deep technical expertise in machine learning and recommendation systems, industry leadership, and strong collaboration skills. You will guide the development of high-impact systems end-to-end—leading innovation from foundational research through production deployment—while mentoring scientists and influencing product and engineering roadmaps. We are looking for a thought leader who brings a strong track record of delivering ML innovations at scale, along with the curiosity and drive to push boundaries. This is a rare opportunity to drive meaningful impact at one of the largest streaming services in the world. Key job responsibilities -Invent, prototype, and productionize large-scale AI solutions across Prime Video’s personalization and discovery ecosystem using deep learning, generative AI, reinforcement learning, and optimization techniques; -Provide technical leadership and influence product vision by collaborating closely with engineers, product managers, and senior stakeholders; -Design and lead high-impact A/B tests and data analyses to validate hypotheses and guide product direction; -Drive technical bar-raising across science and engineering teams through mentorship, design reviews, and collaboration; -Stay ahead of industry trends and emerging research; leverage them to evolve long-term strategy and architecture; -Publish impactful research internally and externally (e.g. top-tier conferences and journals). About the team Prime Video Personalization and Discovery (PVPD) is dedicated to creating a highly personalized content discovery experience that not only delights our customers but also drives both short-and long-term business goals. Our scope includes personalized recommendations, search, marketing, and the advanced machine learning technology and infrastructure that underpins these experiences. Our mission is to automate and enhance customer engagement through personalization, using ML and Generative AI. Basic Qualifications: -PhD (or Master’s with 10+ years of applied experience) in machine learning, computer science, statistics, or a related field -8+ years of experience designing, building, and deploying ML models for real-world business applications at scale -Proven leadership in driving cross-functional ML/AI initiatives from ideation through production Proficiency in Python, Java, or C++, and experience with ML frameworks (e.g., TensorFlow, PyTorch) -Deep expertise in recommendation systems, search relevance, or large-scale personalization systems Preferred Qualifications: -Strong publication record in top-tier conferences (e.g. NeurIPS, ICML, KDD, RecSys, SIGIR) -Experience with large-scale distributed systems (e.g. Hadoop, Spark) -Demonstrated ability to influence technical roadmaps and mentor other scientists and engineers -Experience defining and delivering strategic AI vision for consumer-facing products Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.", "summaryDescription" : "Principal Applied Scientist, Prime Video Personalization & Discovery at Amazon.com Services LLC. Skills: Machine learning, Recommendation systems, Generative AI, Personalization. Invent, prototype, and productionize AI solutions. Provide technical leadership", "postedDate" : "2026-06-13T19:45:52.067", "createdAt" : "2026-06-13T19:45:52.067", "lastUpdated" : "2026-06-15T11:06:10.456", "ingestTimestamp" : "2026-06-13T19:45:52.067", "mongoId" : "68ed10ae70f0b99d58e7bcd1", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3104623/apply", "salaryAnnualMin" : 179000, "salaryAnnualMax" : 309400, "salaryBand" : "150k-200k", "hasSalary" : true, "allSkills" : [ "Recommendation systems", "Search relevance", "Personalization systems", "A/B testing", "Data analysis", "Leadership", "Mentoring", "Machine learning", "Generative AI", "Personalization", "System design", "MLOps", "Discovery ecosystem" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Executive", "dataQualityScore" : 1.0, "_score" : 678.39136, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2a5b9174d8ee00a0610fbf", "title" : "Principal Data Scientist, Health Informatics", "company" : "Waymark", "industry" : "Healthcare", "salary" : { "min" : 220000.0, "max" : 350000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline data for Principal Data Scientist in the US Healthcare industry, adjusted for executive seniority and remote work." }, "location" : "US - Remote ", "locations" : [ "US - Remote" ], "timestamp" : "2026-06-13T10:48:39.355", "fingerprint" : "708199bccfbcbfec0d73600f8b8da1c14f508d5c3da73ffa649e4b7bf35fd618", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/waymark/jobs/4703537005", "externalId" : "4703537005", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "Python, FHIR, HL7, C-CDA, EHR, ADT, ML, AI, LLM", "professionalSkills" : [ "Health economics", "Clinical data", "ML/AI", "Health informatics", "Data quality", "Data normalization", "Clinical validity", "Risk adjustment", "Utilization measurement", "Cost attribution", "Quasi-experimental evaluation", "Outcomes measurement" ], "hardSkillsList" : [ "Clinical terminologies", "Value set curation", "Model validation", "Model deployment", "ML pipelines", "Model versioning", "Reproducible workflows" ], "hardSkills" : [ { "name" : "Clinical terminologies", "level" : "intermediate" }, { "name" : "Value set curation", "level" : "intermediate" }, { "name" : "Model validation", "level" : "intermediate" }, { "name" : "Model deployment", "level" : "intermediate" }, { "name" : "ML pipelines", "level" : "intermediate" }, { "name" : "Model versioning", "level" : "intermediate" }, { "name" : "Reproducible workflows", "level" : "intermediate" } ], "majorSkills" : "Clinical data, Applied ML/AI, Health economics", "majorSkillsList" : [ "Clinical data", "Applied ML/AI", "Health economics" ], "coreResponsibilities" : "Define standards for clinical data, Structure clinical data, Normalize clinical data, Validate clinical data, Build production ML/AI models, Validate production ML/AI models, Deploy production ML/AI models, Translate data into evidence, Advance machine and AI products, Set standards for team, Mentor junior data scientists", "teamCollaboration" : "Data science stakeholders; Engineering stakeholders; Product stakeholders; Clinical stakeholders", "projectManagementSkills" : [ "Project ownership" ], "domainKnowledge" : "Claims data; EHR data; ADT data; Clinical terminologies; ICD-10; SNOMED CT; LOINC; RxNorm; CPT/HCPCS; Healthcare data standards; Health economics; Outcomes methods; CMS reporting; NCQA reporting; MCO reporting; Medicaid populations; Dual-eligible populations", "domainList" : [ "claims-data", "ehr-data", "adt-data", "clinical-terminologies", "icd-10", "snomed-ct", "loinc", "rxnorm", "cpt-hcpcs", "healthcare-data-standards", "health-economics", "outcomes-methods", "cms-reporting", "ncqa-reporting", "mco-reporting", "medicaid-populations", "dual-eligible-populations" ], "domainSpecificKnowledge" : [ "Claims data", "EHR data", "ADT data", "Clinical terminologies", "ICD-10", "SNOMED CT", "LOINC", "RxNorm", "CPT/HCPCS", "Healthcare data standards", "Health economics", "Outcomes methods", "CMS reporting", "NCQA reporting", "MCO reporting", "Medicaid populations", "Dual-eligible populations" ], "toolProficiency" : [ "Epic", "Cerner", "Athena", "MLflow", "SageMaker Pipelines" ], "innovationCreativity" : "Generative AI products", "outcomesObjectives" : "Drive better care; Translate clinical data into predictions; Translate clinical data into outcomes evidence", "qualificationsBasic" : "Master's degree in Data Science, Biostatistics, Health Informatics, Computer Science, or a related field, 7-8+ years of hands-on experience in Python, Healthcare Data Expertise, Standards Fluency", "qualificationsPreferred" : "PhD in health informatics, statistics, data science, or computer science, Experience integrating EHR/HIE data via TEFCA, CommonWell, or comparable networks, Health Economics & Outcomes Methods experience, Prior experience building on Medicaid or dual-eligible populations, Peer-reviewed publications in healthcare ML, AI, biostatistics, or health economics", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Principal Data Scientist, Health Informatics Waymark is a team of healthcare providers, technologists, and builders whose mission is to bring the best healthcare to people with Medicaid benefits. Guided by the communities we serve, we bring support and technology-enabled care to help primary care providers keep Medicaid patients healthy. We are building the tools and designing an approach to enable care to reach the patients who can benefit most. Our core values embody the essence of what makes Waymark a unique team today, and what we look for, nurture, and sustain as a team. We are bold builders, believing that the greatest challenges in care delivery can be solved when we harness the power of community and technology. We are humble learners, seeking feedback and perspectives different from our own, and welcome challenges to our conclusions. We experiment to improve, actively seeking data to inform decisions and assess our own performance. We act with focused urgency, our commitment to our mission drives us to tirelessly pursue results. About This Role Waymark is seeking a Principal Data Scientist to own clinical data as a first-class input to modeling and to bring senior ML/AI and health economics judgment to our core data science products. As Waymark scales across health plan and health system partners, clinical data quality directly determines model accuracy. We need a senior owner accountable for data quality, normalization, and clinical validity across claims, EHR, and ADT. This role sits at the intersection of clinical data expertise, applied ML/AI, and health economics methods. You will own the clinical data strategy that enables our modeling, defining how EHR and ADT data, across formats including FHIR, HL7v2, and C-CDA, should be structured, normalized, and validated as modeling inputs, with hands-on fluency in how these systems are structured and what the data actually represents clinically. You will build and ship production models that advance our existing machine learning and generative AI products, and operate as a senior technical leader, making architectural trade-offs, aligning data science, engineering, product, and clinical stakeholders, and raising the technical bar of the team. This is a highly versatile role for someone who is equally fluent in clinical terminologies and production ML, and who can move work from prototype to deployment with rigor and speed. Responsibilities Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources. Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity. Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards. Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships. Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team. Minimum Qualifications Healthcare Data Expertise: Deep, hands-on fluency with claims, EHR, and ADT data, and strong command of clinical terminologies (ICD-10, SNOMED CT, LOINC, RxNorm, CPT/HCPCS) and value set curation. Standards Fluency: Working experience with healthcare data standards and exchange formats — FHIR, HL7v2, and C-CDA. Education: Master's degree in Data Science, Biostatistics, Health Informatics, Computer Science, or a related field. Python Proficiency: 7-8+ years of hands-on experience in Python, including data science and ML libraries. Applied ML/AI Experience: Demonstrated ability to build, validate, and deploy production ML models on healthcare data, with end-to-end ownership from development through deployment and maintenance in a live environment. Experience with ML pipelines, model versioning, and reproducible workflows at scale. Project Ownership: Proven ability to manage complex technical projects independently, align multiple stakeholders, and deliver on timelines. Preferred Qualifications PhD in health informatics, statistics, data science, or computer science Experience integrating EHR/HIE data via TEFCA, CommonWell, or comparable networks. Health Economics & Outcomes Methods: Experience with risk adjustment, utilization and cost measurement, and quasi-experimental evaluation. Familiarity with MLOps best practices including experiment tracking and model registry (e. g. MLflow), CI/CD for ML pipelines, feature stores, and workflow orchestration tools such as SageMaker Pipelines. Prior experience building on Medicaid or dual-eligible populations. Peer-reviewed publications in healthcare ML, AI, biostatistics, or health economics. Why This Role Matters Waymark is scaling across health plan and health system partners, and the depth of clinical insight we can extract from our data directly determines whether our models drive better care. This role sits at the center of what makes Waymark's models accurate and clinically actionable. By taking ownership you will: Define and own clinical data quality standards across claims, EHR, and ADT. Build and ship production ML/AI models that translate clinical data into actionable predictions and outcomes evidence Advance our core DS and AI products with production-grade models and rigorous methods Raise the technical bar of the data science team through standards-setting and mentorship Hiring Range US Employees in San Francisco/Bay Area, New York City - $160,000 - $229,000 US Employees in Boston, Los Angeles, Seattle, Washington DC - $147,000 - $211,000 US Employees in Arlington, Denver, San Diego, Sacramento - $140,800 - $202,000 US Employees in Albany, Atlanta, Austin, Baltimore, Central/Southern, Charlotte, Chicago, Dallas/Fort Worth, Detroit, Houston, Las Vegas, Miami, Milwaukee, Philadelphia, Portland, Research Triangle, Salt Lake City, Twin Cities - $128,000 - $184,000 US Employees in Baton Rouge, Birmingham, Charleston, Cincinnati, Cleveland, Daytona Beach, Indianapolis, Nashville, New Orleans, Omaha, Phoenix, Pittsburgh, St. Louis, Tampa - $124,160 - $178,000 In addition to salary, we offer a comprehensive benefits package. Here’s what you can expect: Stock Options: Opportunity to invest in the company’s growth. Work-from-Home Stipend: A dedicated stipend for your first year to help set up your home office. Medical, Vision, and Dental Coverage: Comprehensive plans to keep you and your family healthy. Life Insurance: Basic life insurance to give you peace of mind. Paid Time Off: 20 vacation days, accrued over the year, plus 11 paid holidays. Parental Leave: 16 weeks of paid leave for birthing parents after six months of employment, and 8 weeks of bonding leave for non-birthing parents. Retirement Savings: Access to a 401(k) plan with a company contribution, subject to a vesting schedule. Commuter Benefits: Convenient options to support your commute needs. Professional Development Stipend: A dedicated stipend supports professional development and growth. Offer of employment is contingent upon successful completion of a background check. Employment history and advance degree verification (when applicable) are included as part of the standard background check process. Don’t check off every box in the requirements listed above? Please apply anyway! Studies have shown that some of us may be less likely to apply to jobs unless we meet every single qualification. Waymark is dedicated to building a supportive, equal opportunity, and accessible workplace that fosters a sense of belonging – so if you’re excited about this role but your past experience doesn’t align perfectly with every preferred qualification in the job description, we encourage you to still consider submitting an application. You may be just the right candidate for this role or another one of our openings!", "summaryDescription" : "Principal Data Scientist, Health Informatics at Waymark. Skills: Clinical data, Applied ML/AI, Health economics. Define standards for clinical data. 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Example: Professional Engineer (PE or P.Eng) license. Extensive experience in project execution Proficient computer skills in Microsoft Office as well as standard design software. Must be comfortable being viewed as a technical expert in engineering analysis and design and have proven background of technical leadership on these types of projects An attitude and commitment to being an active participant of our employee-owned culture What We Believe HDR is our company. Together, we build on each other's life experiences and perspectives to make great things possible every day. This shapes our collaborative culture, encourages organizational trust and connects us closer to the clients and communities we serve. Our Commitment As employee owners, we all have a role in creating an inclusive environment where each of us is welcomed, valued, respected and empowered to bring our authentic selves to work every day. Our eight Employee Network Groups (Asian Pacific, Black, Hispanic/Latino(a), LGBTQ+, People with Disabilities, Veterans, Women, Young Professionals) help create a sense of belonging and foster a supportive environment where everyone is empowered to engage and contribute. Each group has an executive sponsor and is open to all employees. At HDR, the health and safety of our employees is of the upmost importance. In accordance with local safety, health, and environmental legislation, HDR is committed to developing, maintaining, and enforcing proactive safe work policies, procedures, and standards. In accordance with the Accessibility for Ontarians with Disabilities Act (AODA) and other applicable legislation, we are committed to meeting the accessibility needs of persons with disabilities. Candidates with disabilities may request reasonable accommodation throughout the recruitment and selection process by contacting our Human Resources team. 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Leeds, England, United Kingdom; Manchester, England, United Kingdom; London, England, United Kingdom; United Kingdom; Bulgaria", "employmentType" : "FULL_TIME", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "technicalSkills" : "AI, Claude Code, Claude Opus", "professionalSkills" : [ "System design", "Architecture", "Decision making", "Review judgement", "Force multiplier", "SME", "Intellectual honesty", "Pragmatism", "Bias toward delivery", "Curiosity", "Context engineering", "Knowledge bases", "Machine-readable specs", "Non-determinism", "Value stream", "Fallback path" ], "hardSkillsList" : [ "Deep engineering foundations", "System thinking", "AI fluency", "Context engineering", "Code review", "Tool selection", "Non-determinism handling", "Evals", "Golden datasets", "Structured outputs" ], "softSkillsList" : [ "Outcome obsessed", "Intellectually honest", "Deeply pragmatic", "Relentlessly curious" ], "hardSkills" : [ { "name" : "Deep engineering foundations", "level" : "intermediate" }, { "name" : "System thinking", "level" : "intermediate" }, { "name" : "AI fluency", "level" : "intermediate" }, { "name" : "Context engineering", "level" : "intermediate" }, { "name" : "Code review", "level" : "intermediate" }, { "name" : "Tool selection", "level" : "intermediate" }, { "name" : "Non-determinism handling", "level" : "intermediate" }, { "name" : "Evals", "level" : "intermediate" }, { "name" : "Golden datasets", "level" : "intermediate" }, { "name" : "Structured outputs", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Outcome obsessed" }, { "name" : "Intellectually honest" }, { "name" : "Deeply pragmatic" }, { "name" : "Relentlessly curious" } ], "majorSkills" : "Architecture, Deep engineering, AI fluency, System thinking", "majorSkillsList" : [ "Architecture", "Deep engineering", "AI fluency", "System thinking" ], "coreResponsibilities" : "Set AI up for success, Architect systems with clean interfaces, Review code, Pick right tool for job, Build with AI components, Handle non-determinism, Build with guardrails, Build with quality gates, Justify technical opinions commercially, Immerse in client environment", "teamCollaboration" : "Cross-functional teams; Client sites; CreateFuture offices", "projectManagementSkills" : [ "Roadmap planning" ], "domainKnowledge" : "Enterprise clients; Scale-up businesses; AI models; Inference calls", "domainList" : [ "enterprise-clients", "scale-up-businesses", "ai-models", "inference-calls" ], "domainSpecificKnowledge" : [ "Enterprise clients", "Scale-up businesses", "AI models", "Inference calls" ], "toolProficiency" : [ "Claude Code", "Claude Opus" ], "communicationSkills" : "Hold a room; Client SME", "documentationSkills" : "Specs; Knowledge bases; Machine-readable specs", "continuousLearning" : "Professional growth; Personal growth", "innovationCreativity" : "R&D projects", "problemSolvingSkills" : "Root cause analysis; Troubleshooting; System thinking", "specialRequirements" : "Occasional travel, On-site work", "outcomesObjectives" : "Produce quality work at speed; Compound into systems that work; Unblock value stream", "qualificationsBasic" : "Complete package: architecture, Deep engineering foundations, System thinking, Judgement to direct intelligence, AI as first-class competency, Context engineering as craft, Architect systems with clean interfaces, Read far more code than write, Pick right tool for job, Build with AI components, Fluent with AI, On-site work", "qualificationsPreferred" : "Client enterprise experience, Scale-up business experience", "remoteFriendly" : true, "workSiteFlexibility" : "Flexible", "rawDescription" : "CreateFuture is fast becoming the UK’s most recognisable digital consultancy, with years of experience building digital products and services for major organisations whilst putting our people first. We have offices in the centre of Edinburgh, Leeds, Manchester and London as well as remote employees located throughout the country. We are a team of creators - whether that’s code, project plans, go to market strategies, culture initiatives, marketing campaigns, large language models or people policies. And together, with our clients, we create the future. This has seen us collaborate and partner across a multitude of industries and sectors, with the likes of PayPal, Adidas, NatWest, FanDuel and Money Saving Expert to name just a few. Our reputation as a partner determined to deliver high-quality, robust and thoughtful products has enabled us to scale to over 500 people in the last couple of years, and it is our amazing people - along with the safe, supportive and friendly culture we have built - that makes CreateFuture a great place to work. Don’t just take our word for it though, we have been recognised by Best Workplaces UK multiple years in a row - across a number of categories - and our employee exit rate is astonishingly low. Join us on our journey… Let’s create something awesome, together, today. Principal Engineer About this role The bar has moved. Five years ago a strong engineer was measured by what they could build and how well they could mentor others to build it. Today, with Claude Code and agents, we produce quality work at a speed that would have looked superhuman in 2022. What separates exceptional from good is no longer typing speed or local craftsmanship. It is the complete package: architecture, deep engineering foundations, communication, system thinking, and the judgement to direct both your own intelligence and AI toward outcomes that matter. Exceptional people at this level are not the best implementer in the room; they are the person whose specifications, decisions, and review judgement compound into systems that work in production and that others (and agents) can build on. They are a force multiplier, they hold a room at the highest levels, and clients treat them as the SME by default. Your mindset The mindset is the engine, and it is almost impossible to teach retrospectively. You are outcome obsessed, not output obsessed and you care whether the problem was actually solved - you resist the ego instinct to admire a clever solution and ask whether it was needed at all. You are intellectually honest about uncertainty, taking a trust-but-verify approach and assuming the surprising bug is the one that will burn. You are deeply pragmatic, which matters at CreateFuture where pace counts: you have outgrown the religious wars about platforms, languages and patterns, you hold opinions loosely, and you decide for the systems in front of you, not the ones you wish you had. You have a bias toward delivery: the smallest thing that works, then iterate, treating documents and meetings as means to that end. And you are relentlessly curious beyond any immediate need, wanting to know how the database actually stores things or why a Claude Opus inference call costs what it does, which keeps you several layers ahead while the rest of the team is still reading the API docs. AI as the accelerant For a Principal to be truly exceptional, this is now already a first-class competency in your arsenal, not a side skill. An engineer who is brilliant by last year's standards but not fluent with AI is not exceptional in 2026. In practice that means: Setting AI up to succeed. You treat context engineering as a craft (the right files, specs, examples and constraints), and you architect systems with clean interfaces, good naming, machine-readable specs and knowledge bases so the codebase is a quality baseline AI can work on later. No separate ivory-tower architect required. Reviewing and choosing well. You read far more code than you write, and your value is in the quality of review: catching the silent assumption, the security flaw, the architectural drift, knowing where models fail and looking there first. You pick the right tool for the job (Claude Code for a deep refactor, an inline assistant when that is enough, a sub-agent for parallel work, the right model for analysis versus generation) and you know the constraints differ by client, an enterprise like PayPoint or Natwest versus a scale-up type business like Zuto or OAG. Working with non-determinism. You see where AI moves the bottleneck and help people left and right of you unblock the value stream. You build with AI components but know success is no longer a binary, reaching for evals, golden datasets, structured outputs and fallback paths as naturally as you reach for unit tests. Technical breadth across the full stack Exceptional means depth in at least one area but competence everywhere the system reaches. Backend: an instinct for distributed systems (what fails, what is eventually consistent, where queues back up), APIs that age well, thoughtful data modelling, and an understanding that languages are mostly syntax differences you can close with AI. Cloud: cost-aware and security-aware by default, reaching for IaC, composing the primitives rather than stitching together managed services and hoping it works. Data: modern pipelines, governance and lineage, increasingly vector stores and embeddings, with data quality treated as seriously as production code because garbage in, garbage out is far worse once AI features depend on it. Web and mobile: past the framework arguments, with a clear view on what you would pick today, and able to build \"good enough\" front ends yourself with AI. Full-stack here means seeing the platform as one system and knowing which levers matter most. It does not mean front-end work with a few Node.js servers. System thinking, consulting and commercial judgement At CreateFuture we work in a consultancy context, where many otherwise excellent IC engineers can struggle. You know technology is roughly 30% of the picture; the rest is design, argument and reasoning, commercial and political acumen, and the clarity to frame outcomes for a COO, CTO, CIO or CEO in their own language. You know when gold-plating is wasteful, you can name when a project's real problem is not technical and say so even when it is awkward, and you have a strong sense of what is durable versus what is fashion. You are not the engineer who introduced the team to seven new technologies last year; you are the one who removed three and quietly made the remaining stack stronger. Leadership mentality Technical work is necessary but no longer sufficient. By default you raise the level of everyone around you, regardless of discipline. Communicate clearly and frequently (documents, decisions, post-mortems, design notes); the written artefact is the machine-readable context we can feed to AI later. Make decisions and own them. You don't escalate everything or hoard control, and you are willing to be the benevolent dictator when it is your turn. Mentor in AI fluency, not just code: tooling, prompting and review discipline, so your team is twice as productive a year from now. Need little line management. You communicate upwards, raise by exception, self-resolve, and can be trusted with financial controls. Disagree well. You tell people they are wrong with evidence and no ego, and you change your own mind when you are shown to be wrong. What we're not looking for The gifted engineer who sits in a silo. This is a team game. The one who is fluent with AI but ships without the guardrails and quality gates enterprise clients expect. The one with strong technical opinions who cannot justify them commercially and gets defensive when challenged. The pure manager who wants to orchestrate teams rather than get involved. This is about setting a new standard, not coordinating it. The engineer who will not immerse in a client environment. This role will mean on-site work and trusted relationships built in person. We are not going back to working away from the client and being bypassed. What we’ll offer you: We trust people to do their best work. That means flexibility over rigid rules, impact over activity, and real investment in your growth both professionally and personally. You’ll be part of a supportive, and friendly culture, surrounded by smart, curious people who care deeply about what they do. We offer flexible working, including hybrid and remote options. Our office hubs are located in Edinburgh, Leeds, Manchester, London and Bulgaria, with occasional travel to client sites or CreateFuture offices when needed. We trust you to manage your time balancing collaboration with client time and focused work. What matters is the impact you have, not how busy you look. Our hiring process We try to keep our hiring process clear, fair and respectful of your time. We aim to get back to everyone who applies and we will be upfront about where you are in the process. It usually looks like this: Call with our Talent Acquisition Team Role specific capability interview Depending on the role, we might also ask you to do a short presentation, a practical or technical task or have a values focused conversation. We will explain what is involved before anything happens. Inclusion at CreateFuture We believe diverse teams build better workplaces and better products. We want CreateFuture to be a place where people feel able to be themselves and do their best work. If you need any adjustments or support during the application process, just. We will do what we can to help. We look forward to your application!", "summaryDescription" : "Principal Engineer at CreateFuture. Skills: Architecture, Deep engineering, AI fluency, System thinking. Set AI up for success. Architect systems with clean interfaces", "postedDate" : "2026-06-14T04:24:28.772", "createdAt" : "2026-06-14T04:24:28.772", "lastUpdated" : "2026-06-15T05:24:45.498", "ingestTimestamp" : "2026-06-14T04:24:28.772", "mongoId" : "6a2c79f174d8ee00a0943d36", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "100k-150k", "hasSalary" : false, "allSkills" : [ "Deep engineering foundations", "System thinking", "AI fluency", "Context engineering", "Code review", "Tool selection", "Non-determinism handling", "Evals", "Golden datasets", "Structured outputs", "Outcome obsessed", "Intellectually honest", "Deeply pragmatic", "Relentlessly curious", "Architecture", "Deep engineering", "System design", "Decision making", "Review judgement", "Force multiplier", "SME", "Intellectual honesty", "Pragmatism", "Bias toward delivery", "Curiosity", "Knowledge bases", "Machine-readable specs", "Non-determinism", "Value stream", "Fallback path", "Roadmap planning", "Enterprise clients", "Scale-up businesses", "AI models", "Inference calls", "Claude Code", "Claude Opus" ], "skillCount" : 37, "jobCategory" : "engineering", "jobSubcategory" : "software", "seniorityLevel" : "Executive", "dataQualityScore" : 1.0, "_score" : 675.1807, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.95 } ], "status" : 200, "message" : "OK", "pagination" : { "page" : 0, "limit" : 50, "totalCount" : 86151, "totalPages" : 1724, "hasNext" : true, "hasPrevious" : false } }

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