Senior Applied Scientist, Leo Satellite Build Intelligence

Amazon • Long Beach, CA

6d • Onsite

About The Position

Build the scientific intelligence layer powering Amazon’s satellite manufacturing system. We are looking for a Senior Applied Scientist to lead the development of models that transform fragmented manufacturing, test, quality, and operational data into a unified, closed-loop intelligence system that directly improves how satellites are built. You will work on high-ambiguity problems where data is incomplete, noisy, and distributed, and where model outputs directly influence real-world manufacturing decisions. Your work will power AI-native workflows such as non-conformance disposition, root-cause analysis, and predictive test optimization, reducing defects, accelerating production, and enabling self-improving manufacturing systems.

Requirements

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Experience training and evaluating machine learning models on large-scale, real-world datasets
• Experience applying statistical analysis and experimentation to measure model performance and drive improvements
• Experience working with engineering teams to deploy machine learning models into production systems
• Must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Nice To Haves

• Experience training and deploying LLM-based systems, retrieval-augmented generation (RAG), or agentic workflows
• Experience designing evaluation frameworks for production AI systems, including safety, grounding, and regression testing
• Experience building closed-loop or feedback-driven ML systems
• Experience working with ambiguous problem spaces and inventing novel modeling approaches
• Experience influencing scientific direction across teams and mentoring other scientists
• Experience in manufacturing, aerospace, robotics, or other complex physical-world systems
• Experience working with governed data environments, compliance constraints, or access-controlled systems
• Experience building systems where model outputs directly drive operational or physical-world decisions

Responsibilities

• Lead the design, training, and deployment of machine learning models, including LLM-based systems, retrieval models, and task-specific models
• Translate ambiguous, real-world manufacturing problems into well-defined scientific problems, modeling approaches, and evaluation criteria
• Train, fine-tune, and evaluate models using large-scale, noisy, and heterogeneous datasets with incomplete or delayed ground truth
• Develop models over partially observed systems spanning test data, inspection signals, quality records, supplier data, and knowledge systems
• Invent and extend approaches for problems such as anomaly detection, root-cause inference, multimodal learning, and generative AI under real-world constraints
• Define evaluation frameworks that capture real-world failure modes, distribution shift, and decision risk, and use them to drive model iteration
• Make principled tradeoffs between model complexity, data quality, and generalization, and justify when to extend or depart from state-of-the-art approaches
• Work closely with engineering teams to deploy models into production systems with monitoring, feedback capture, and continuous retraining
• Build closed-loop learning systems where model outputs influence design, manufacturing, and test decisions
• Influence scientific direction across teams and mentor scientists and engineers

Benefits

• 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
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior