[pipeline] Staff+ Software Engineer, Experimentation

Anthropic Anthropic · AI Frontier · New York, NY +1 · Software Engineering - Infrastructure

This role focuses on building and maintaining the configuration and experimentation infrastructure that enables engineers and researchers at Anthropic to safely roll out changes, run controlled experiments, and make data-driven decisions across their AI products and infrastructure. It involves feature flagging, dynamic configuration, and A/B testing platforms supporting both research and production workloads.

What you'd actually do

  1. Own the technical strategy and roadmap for config and experimentation infrastructure, translating team-level goals into concrete execution plans and partnering with teams focused on deployment, testing, and more
  2. Maintain and enhance the architecture for feature flagging, dynamic configuration, and experimentation systems, ensuring the hardest problems get solved — whether by you directly or by working through others
  3. Design and build scalable, reliable distributed infrastructure and shared libraries that support high-volume experimentation and config workloads across all engineering teams
  4. Own and evolve the platforms and tooling that let engineers and researchers safely ship config changes, run experiments, and measure impact
  5. Define standards, tooling, and frameworks for experimentation and configuration management that drive developer productivity across research and production workloads

Skills

Required

  • Python
  • Rust
  • Go
  • container orchestration
  • infrastructure at scale

Nice to have

  • Statsig
  • GrowthBook
  • LaunchDarkly
  • Optimizely
  • Unleash
  • randomization
  • assignment
  • metrics pipelines
  • statistical analysis
  • gradual rollouts
  • kill switches
  • targeting rules
  • CLI tools
  • developer-facing services
  • APIs/automation workflows
  • CI/CD pipelines

What the JD emphasized

  • configuration management
  • feature flagging
  • experimentation platforms
  • large-scale environment
  • container orchestration
  • infrastructure at scale
  • ambiguous, high-impact technical challenges