Senior Applied Scientist, Leo Satellite Build Intelligence

Amazon · Long Beach, CA · 3 wk ago

ManagementFull-time

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

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

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

Benefits