Applied Scientist, Aws Quick

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

This role is for an Applied Scientist on the AWS Agentic AI team, focusing on building next-generation models for intelligent automation. The role involves defining and implementing automated reasoning features, applying software engineering best practices, and delivering high-quality scientific artifacts. The ideal candidate has experience in autonomous agents, API orchestration, planning, large multimodal models, reinforcement learning, and sequential decision making, with a strong publication record.

What you'd actually do

  1. Define and implement new automated reasoning features that employ scalable and efficient approaches to solve complex problems using neural learning and symbolic/formal reasoning
  2. Apply software engineering best practices to ensure a high standard of quality for all team deliverables
  3. Work in an agile, startup-like development environment
  4. Deliver high-quality scientific artifacts
  5. Work with the team to help drive business decisions

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • 3+ years of building models for business application experience

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • publish your findings at peer reviewed conferences and workshops
  • experience in patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • building the next generation models for intelligent automation
  • develop innovative solutions to hard problems
  • publish your findings at peer reviewed conferences and workshops
  • autonomous agents
  • API orchestration
  • Planning
  • large multimodal models
  • reinforcement learning
  • sequential decision making