Software Development Engineer Ii, Automated Reasoning Group

Amazon Amazon · Big Tech · NY +1 · Software Development

Software Development Engineer II role focused on building and enhancing services that use Automated Reasoning to verify Generative AI outputs, specifically addressing hallucinations. The role involves designing and implementing complex distributed systems, owning components end-to-end, and collaborating with various teams to translate requirements into technical solutions.

What you'd actually do

  1. Design and implement complex distributed systems with stringent latency and reliability requirements
  2. Own critical components end-to-end, from architecture and design through deployment and operations
  3. Drive technical alignment across multiple engineering teams and stakeholders
  4. Collaborate with Product, Science, and customers to translate requirements into effective technical solutions
  5. Develop peers through mentorship, knowledge sharing, and best practices

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • 1+ years of software development engineer or related occupational experience
  • 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
  • 1+ years of Object Oriented Design experience
  • Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent

What the JD emphasized

  • stringent latency and reliability requirements
  • critical components end-to-end
  • technical alignment across multiple engineering teams
  • translate requirements into effective technical solutions

Other signals

  • Automated Reasoning Checks (ARc)
  • verify the accuracy of Generative AI outputs
  • tackling hallucinations
  • combining AI and math to give customers confidence in their Generative AI applications