Senior Applied Scientist, Leo Satellite Build Intelligence

About the role

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

• Design and deploy purpose-built models that power production-critical decisions across satellite manufacturing.
• 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.
• 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

• 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 Qualifications

• 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.