Principal Applied Scientist, Automated Reasoning

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

This role leads research and development in automated reasoning, formal verification, and program analysis to make formal methods practical for cloud-scale systems. It involves designing novel algorithms, mentoring scientists, and collaborating with product teams to transition research into production. A key focus is pioneering the use of these techniques for agentic systems to ensure AI agents operate within safety boundaries.

What you'd actually do

  1. Lead research initiatives in automated reasoning, formal verification, SMT solving, model checking, or program analysis
  2. Design and implement novel algorithms and techniques that advance the state of the art
  3. Mentor and guide applied scientists, research scientists, and engineers
  4. Collaborate with product teams to transition research into production systems
  5. Define technical vision and strategy for automated reasoning initiatives

Skills

Required

  • MS or Ph.D. degree in Electrical Engineering, Computer Science, Mathematics, or related technical field
  • Industrial/academic experience in formal verification, and theorem proving
  • Experience with program analysis, program verification or synthesis
  • Experience with programming languages such as Rust, C, C++, Java, Dafny, OCaml or Haskell, and open source technologies
  • Experience in design and analysis of algorithms and data structures

Nice to have

  • Ph.D. degree in Electrical Engineering, Computer Science, Mathematics, or related technical field
  • Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements
  • Excellent written and verbal technical communication with an ability to present complex technical information in a clear and concise manner to a variety of audiences
  • Exceptional customer relationship skills including the ability to discover the true requirements underlying feature requests, recommend alternative technical and business approaches, and lead engineering efforts
  • 5+ years of professional software engineering practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
  • Familiarity with machine learning and generative AI techniques

What the JD emphasized

  • automated reasoning
  • formal verification
  • program analysis
  • agentic systems
  • safety boundaries

Other signals

  • automated reasoning
  • formal verification
  • agentic systems
  • safety boundaries