Applied Scientists in AWS Automated Reasoning develop and apply bleeding-edge formal methods, automated reasoning techniques, and neurosymbolic approaches to ensure the security, reliability, and correctness of Amazon/AWS services and customer applications. Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality.

At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Application areas span cloud infrastructure verification, cryptographic assurance, AI safety, drone safety, and formal guarantees for generative AI systems. Our methods range from interactive theorem proving and constraint solving to neuro-inspired proof search

This is a unique opportunity to get in early on a fast-growing segment of the business and help shape the technology, product, and business. You will have a chance to utilize your deep technical expertise within a fast-moving environment and make a large business and customer impact.


Key job responsibilities
• Design and implement algorithms and formal methods for automated reasoning, including constraint solving, model checking, static analysis, theorem proving, and program synthesis to verify the correctness, security, and reliability of computing systems.
• Solve large or significantly complex problems that require deep knowledge and scientific innovation in your domain; own strategic problem solving and take the lead on design, implementation, and delivery of solutions with long-term quantifiable impact.
• Develop new decision procedures, heuristics, and search strategies that improve the scalability and accuracy of verification tools; build and deploy production-grade automated reasoning systems at Amazon scale.
• Explore and apply generative AI and machine learning techniques to enhance automated reasoning capabilities, including learning-based heuristics for search and optimization, neural approaches to symbolic reasoning, and methods for verifying the correctness of AI-generated code.
• Develop automated reasoning techniques for generative AI and agentic coding systems, including methods for ensuring the safety and alignment of autonomous software agents and applying formal guarantees to large language model outputs.
• Conduct original research snd publish findings in peer-reviewed venues.
• Work with customer teams to understand the nature of their software and the properties they need to establish; identify tools and methods capable of addressing verification needs, including novel analysis capabilities.
• Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing deep knowledge and experience; collaborate with partner teams to translate verification capabilities into production systems.
• Mentor scientists and engineers on formal methods, neurosymbolic techniques, and best practices for building reliable automated reasoning systems; assist in career development of others.

Basic Qualifications

- PhD or equivalent research experience
- Experience in any of the following areas: mathematical logic, formal verification, satisfiability solving (eg SAT/SMT), mechanical theorem proving, model checking, or program analysis

Preferred Qualifications

- Experience in professional software development
- Experience applying formal methods or automated reasoning to real-world software, security, or systems problems
- Experience with proof assistants or verification languages such as Lean, Dafny, Isabelle, or Rocq
- Experience programming in OCaml, Haskell, Rust, Scala, Kotlin, or a related language

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