Sr Manager, Applied Science, Aws Compliance & Security Assurance

Amazon Amazon · Big Tech · NY +1 · Applied Science

This role leads a team focused on building and operating automated reasoning technology for security and privacy assurance across Amazon and AWS. The team's core technology is a static analysis platform integrated into critical security workflows, impacting the security posture of AWS services. The role involves technical leadership, hands-on contribution to research and design, team management, product integration, and advancing the state of the art in static program analysis.

What you'd actually do

  1. Own the science roadmap for our automated reasoning engine, including taint analysis, compositional heap analysis, modular method summarization, and dataflow graph generation
  2. Personally contribute to key research and design decisions, including prototyping novel analyses and reviewing technical artifacts
  3. Hire, develop, and retain a world-class team of applied scientists; foster a culture of scientific rigor, innovation, and operational excellence
  4. Partner with application security and service teams to expand our platform's integration footprint and deliver new security and privacy analysis capabilities
  5. Advance the state of the art in static program analysis, including exploring formal verification of analysis correctness (e.g., using Lean, Coq, or Dafny), expanding language support beyond Java, and developing novel analysis techniques for emerging security properties

Skills

Required

  • Master's degree in Computer Science (Machine Learning, AI, Statistics, or equivalent)
  • 10+ years of experience in software development or applied research
  • at least 5 years in a technical leadership or management role
  • Deep expertise in one or more of: static program analysis, abstract interpretation, taint analysis, information-flow security, or automated reasoning
  • Proven track record of building and shipping production-grade analysis tools or developer-facing security tooling

Nice to have

  • PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)
  • Experience in any of the following areas: SAT, SMT, mechanical theorem proving, symbolic simulation, programming language type systems, program analysis.
  • Background in application security, encryption verification, or data privacy compliance
  • Track record of translating academic research into production systems with measurable security impact

What the JD emphasized

  • production-grade analysis tools
  • measurable security impact

Other signals

  • automated reasoning technology
  • security and privacy assurance
  • static analysis platform
  • program analysis
  • security workflows