SandboxAQ
AI Solutions
SeniorMachineLearningEngineer,AIGenerationEngine
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optimal for Senior candidates.
“Senior Machine Learning Engineer, AI Generation Engine at SandboxAQ. Skills: Machine Learning, Python, MLOps, Large Quantitative Models. Design data pipelines. Construct data pipelines”
What You'll Achieve.
deliver AI solutions; advances in life sciences; financial services; navigation; cybersecurity; other sectors; build AI-first products; functional, real-world MVPs; rapidly iterate on different potential solutions; build and evaluate new models; focusing on speed and tangible outcomes; ensure model performance aligns with high-level product objectives; optimize LQM performance; optimize predictive accuracy; efficiency; speed; accuracy in a production context; translate high-level business objectives into actionable ML development and deployment roadmaps
Industry & Context.
address some of the world's greatest challenges; tackle the world's epic challenges
What They're Looking For.
Must Have
BS in Software Engineering, Computer Science, or equivalent field of study, 5+ years of postgraduate experience in software development, Experience developing highly-available, performant, scalable ML systems, large-scale data processing pipelines, Python (including the ML stack: PyTorch, TensorFlow, JAX, NumPy, Pandas), Long, successful history of driving the full ML lifecycle, Deep proficiency in MLOps, software best practices, CI/CD for ML, experiment tracking, automated testing, version control for both code and datasets
Nice to Have
MS or PhD in Software Engineering, Computer Science or equivalent experience, Financial simulation or technical experience, risk simulation, tech leadership in a complex space, driving technical design and execution cross-collaboratively across multiple teams and organizations, Experience with scalable software development on cloud computing platforms (e.g., GCP, AWS)
What You'll Do.
Design data pipelines
Construct data pipelines
Manage data pipelines
Analyze model behavior
Tune hyper-parameters
Optimize model architecture
Collaborate with researchers
Collaborate with product managers
Collaborate with SWEs
Enforce engineering standards
How You'll Work.
Team & Collaboration
Collaborate closely with AI researchers, product managers, and SWEs; play a key role in efficient and effective enablement of the cutting-edge technologies; working alongside humble, empowered, and ambitious colleagues
Process & Methodology
driving technical design and execution cross-collaboratively
Full Job Description
ABOUT SANDBOXAQ SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors. We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders. At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact. THE OPPORTUNITY SandboxAQ's AI Generation Engine (SAIGE) team is seeking a highly accomplished Machine Learning Engineer to take ownership of the end-to-end ML lifecycle, from initial data exploration and model development to scalable production deployment. This role is central to designing and rapidly building AI-first products that incorporate Large Quantitative Models (LQMs) and sophisticated agentic frameworks. We are looking for a hands-on engineer who is passionate about owning the entire lifecycle of model development. This requires significant industry experience in bringing machine learning models from conception and experimentation to production and deployment in a robust, scalable manner, including (but not limited to): Data Acquisition and Curation, Infrastructure, Pre-Training, Evaluations, and Fine-Tuning. This person will be one of the founding engineers to join the SAIGE team and will be the bridge between cutting-edge AI concepts and functional, real-world
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