Instructure
Research & Development
SeniorDataScientist,AppliedAI
Neural analysis suggests this role is
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“Senior Data Scientist, Applied AI at Instructure. Skills: Machine Learning Engineering, Applied AI, Production ML/AI Systems, AWS. Architect, build, and deploy production ML/AI systems. Design and operate scalable inference services”
What You'll Achieve.
Scale production AI systems; Turn advanced AI ideas into reliable product capabilities; Improve production performance; Turn prototypes into robust, scalable services; Drive engineering standards; Turn advanced AI research into production value
Industry & Context.
debugging reliability; debugging performance issues
Onsite Collaboration Requirement: This role requires working onsite on Tuesday and Wednesday, with Thursday strongly encouraged as part of our company’s in-person collaboration model., All employees must pass a background check as part of the hiring process., Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws., Any attempt to misrepresent personal or professional information will result in disqualification.
What They're Looking For.
Must Have
6+ years of experience in software engineering, machine learning engineering, applied AI engineering, or a closely related role with production ownership, Demonstrated experience taking ML/AI systems from prototype to production in live environments, experience with deployment pipelines, CI/CD, orchestration, and operating production services on AWS, Experience building and operating APIs/services (Python preferred), working with containers, and debugging reliability/performance issues, Working knowledge of modern AI application patterns (for example, embeddings, retrieval, semantic search, or RAG) and the engineering constraints involved in running them in production, communication skills and the ability to work through ambiguity across engineering, product, and research teams
Nice to Have
Experience building AI-native product features (not just internal analytics models), Experience with vector databases, retrieval infrastructure, or semantic indexing pipelines, Experience with graph databases or graph-based reasoning systems, Experience with observability and evaluation for LLM or retrieval systems (quality metrics, drift, failure analysis), Experience creating internal engineering standards, templates, or reference implementations adopted by multiple teams, Experience in education technology, learning systems, or knowledge/skills modeling, Experience mentoring engineers in a high-growth or platform-building environment
What You'll Do.
and deploy production ML/AI systems
Design and operate scalable inference services
Build and improve data pipelines
Productionize AI workflows with MLOps practices
Define and implement evaluation frameworks
and engineering teams
How You'll Work.
Team & Collaboration
Work closely with product, engineering, and research partners; Partner with product, research, and engineering teams; work through ambiguity across engineering, product, and research teams
Communication Scope
communication skills
Full Job Description
At Instructure, we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers. We do this by giving smart, creative, passionate people opportunities to create awesome. And that's where you come in: Our Advanced Development team builds AI-native capabilities, reusable AI systems, and shared infrastructure that power multiple products and workflows across the platform. We are looking for a Senior Data Scientist to help build, ship, and scale production AI systems from the ground up. This is an engineering-forward ML/AI systems role, intended for candidates who are comfortable moving from prototype to production and can own critical parts of the ML/AI systems lifecycle, including pipelines, model integration, inference services, deployment, monitoring, and operational reliability. You will work closely with product, engineering, and research partners to turn advanced AI ideas into reliable product capabilities used at scale. Important note on scope: This role is not primarily focused on BI/reporting or experimentation analytics. We are looking for someone with strong experience building and operating production ML/AI systems. What You’ll Do - Architect, build, and deploy production ML/AI systems that power customer-facing product capabilities - Design and operate scalable inference services, APIs, and backend components for model-driven applications - Build and improve data, feature, deployment, and orchestration pipelines on AWS across development, staging, and production environments - Productionize AI workflows with strong MLOps practices, including CI/CD, versioning, testing, monitoring, rollback, and operational reliability - Define and implement evaluation frameworks for model quality, system reliability, latency, and cost, and us
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