Sr. Applied Scientist, Applied AI Solutions

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Senior Applied Scientist role focused on designing, developing, and evaluating long-running AI agents for AWS Applied AI Solutions. The role involves building agentic use cases, defining evaluation frameworks for complex agent outputs, and ensuring production deployment. Requires experience in building ML models for business applications and applied research.

What you'd actually do

  1. You will partner with cross-functional business and engineering teams to identify and deliver high-impact agentic use cases across the Applied AI Solutions portfolio.
  2. You will design, develop, and evaluate long-running agents — including orchestration harnesses, memory architectures (episodic recall, semantic facts, revisable beliefs, principled decay), context compaction strategies, and safe, auditable tool and environment designs.
  3. You will define evaluation frameworks for agents whose outputs defy single-answer judgment, building trajectory-level evaluations, reward-hacking detection, and human-in-the-loop review patterns.
  4. As a senior scientist, you will set technical direction, mentor scientists and engineers, and represent the science org in roadmap and architecture decisions.
  5. You will ensure seamless deployment and integration of agents into production systems customers rely on daily.

Skills

Required

  • 3+ years of building machine learning models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Experience with neural deep learning methods and machine learning
  • 5+ years of applied research experience
  • PhD in CS (Computer Science), CE (Computer Engineering), or related technical field, OR MSc + 10 years, OR BSc + 12 years

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.

What the JD emphasized

  • long-running agents
  • evaluation frameworks for agents
  • set technical direction
  • mentor scientists and engineers
  • deployment and integration of agents into production systems

Other signals

  • designing and developing long-running agents
  • defining evaluation frameworks for agents
  • mentoring scientists and engineers
  • ensuring seamless deployment and integration of agents into production systems