About the job
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.
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
Qualifications
Minimum
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
Preferred
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