Sde, Responsible AI

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer to build a platform for AI governance at Amazon scale, ensuring AI systems are fair, safe, transparent, and compliant with regulations. The role involves discovering, classifying, and monitoring AI systems, automating compliance evidence collection, and providing insights to stakeholders. It requires collaboration with Applied Scientists, privacy engineers, and policy experts.

What you'd actually do

  1. Design, develop, and operate distributed services that automatically discover and catalog AI systems across Amazon's infrastructure
  2. Build classification and risk-assessment pipelines that map AI systems to regulatory frameworks and internal compliance requirements
  3. Develop self-service tooling and paved-path integrations that help builder teams demonstrate conformance with responsible AI expectations
  4. Build the infrastructure that hosts and scales ML-powered compliance workflows developed by our Applied Science team
  5. Partner with Privacy, Legal, and RAI Science to translate evolving regulatory requirements into scalable technical solutions

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 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
  • 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
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
  • Experience developing using AI tooling such as Clau

What the JD emphasized

  • Responsible AI
  • AI governance
  • fair, safe, transparent, and compliant
  • evolving global regulations
  • No one has solved AI governance at this scale before
  • automating what would take an army of auditors
  • greenfield opportunity
  • define the technical direction of a new product
  • AI-powered classification agents
  • evolving regulatory landscape
  • builders who are energized by ambiguity
  • define the technical direction of a new product
  • societal impact of ensuring AI systems are developed responsibly
  • regulatory risk frameworks
  • responsible AI expectations
  • ML-powered compliance workflows
  • evolving regulatory requirements
  • regulatory reporting timelines
  • responsible AI dimensions
  • AI governance infrastructure

Other signals

  • building the platform that gives Amazon real-time visibility into the compliance posture of thousands of AI systems
  • automating what would take an army of auditors
  • giving builders the tools to do the right thing without slowing down
  • design and build systems that discover AI workloads across Amazon's fleet, classify them against regulatory risk frameworks, streamline compliance evidence collection through paved-path tooling, and surface actionable insights to both builders and leadership
  • collaborate closely with Applied Scientists developing AI-powered classification agents