Applied Scientist Ii, Amazon Quick

Amazon Amazon · Big Tech · Santa Clara, CA · Applied Science

This role focuses on developing innovative solutions for complex problems using autonomous agents, API orchestration, planning, large multimodal models (especially vision-language models), reinforcement learning, and sequential decision making. The candidate will define and implement new automated reasoning features, apply software engineering best practices, and publish findings at peer-reviewed conferences. The role is part of AWS and aims to solve real-world problems with access to significant data and computational resources.

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

  • 3+ years of building models for business application experience
  • 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

Nice to have

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

What the JD emphasized

  • publications at top-tier peer-reviewed conferences or journals
  • autonomous agents
  • API orchestration
  • Planning
  • large multimodal models
  • reinforcement learning
  • sequential decision making

Other signals

  • develop innovative solutions
  • publish your findings
  • autonomous agents
  • API orchestration
  • Planning
  • large multimodal models
  • reinforcement learning
  • sequential decision making