Senior Applied Scientist, Fintelligence

Amazon Amazon · Big Tech · Bellevue, WA · Data Science

Senior Applied Scientist role at Amazon's FinTech organization, focusing on building and scaling generative AI applications and autonomous agents for financial operations. The role involves developing systems that process financial transactions, extract intelligence from documents, and power agents that learn from customer interactions. Key responsibilities include ensuring AI systems are trusted for compliance, designing agents that improve with user feedback, optimizing inference at scale using tiered models and LLMs, and developing robust evaluation frameworks. The position emphasizes shipping production-ready models, working across the full stack, and solving complex real-world financial problems.

What you'd actually do

  1. Building AI systems that finance teams trust enough to rely on without manual review, where precision isn't a nice-to-have, it's a compliance requirement
  2. Designing agents that learn from user corrections and get measurably better with every interaction, not just at the next model release
  3. Solving inference at massive scale using tiered model architectures, intelligent routing, and small language models that deliver production-grade accuracy at a fraction of frontier model cost
  4. Developing evaluation frameworks that catch quality regressions before customers do and gate every model change before it ships

Skills

Required

  • PhD, or Master's degree and 6+ years of applied research experience
  • 3+ years of building machine learning models for business application experience
  • Experience with neural deep learning methods and machine learning
  • Experience programming in Java, C++, Python or related language

Nice to have

  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • Experience with large scale distributed systems such as Hadoop, Spark etc.
  • Have publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • compliance requirement
  • measurably better
  • massive scale
  • evaluation frameworks
  • production-grade accuracy
  • quality regressions
  • ships to production
  • genuinely hard
  • financial data is messy, regulated, high-stakes
  • naive LLM approaches break down

Other signals

  • building generative AI applications
  • autonomous agents
  • large language models
  • multi-agent systems
  • inference at massive scale
  • evaluation frameworks