Senior Applied Scientist, Leo Satellite Build Intelligence
Amazon · Bellevue, WA · Applied Science
About this role
Amazon is hiring a senior-level Applied Scientist in the machine learning function based in Bellevue, WA. The posting calls out experience with Python, Java, LLMs, RAG. Compensation is listed at $167,100–$226,100 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Bellevue, WA
- Department
- Applied Science
- Posted
- May 13, 2026
More roles at Amazon
Job description
from Amazon careersBuild 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. Export Control Requirement: Due to applicable export control laws and regulations, candidates 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. Key job responsibilities In this role, you will 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…