Research Engineer, Universes at Anthropic

Remote - Remote-Friendly (Travel-Required), San Francisco, CA, Seattle, WA, New York City, NY

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The Universes team at Anthropic is seeking Research Engineers to build next‑generation training environments for capable and safe agentic AI. The role blends research and engineering, requiring implementation of novel approaches, contributing to research direction, designing reinforcement‑learning environments and methodologies, and building evaluations that measure genuine capability. Candidates should be impact‑driven, have strong software engineering skills, and be comfortable with uncertainty while collaborating across research and infrastructure teams.

Salary

USD 500,000 - 850,000

Requirements

Skills

  • Are highly impact-driven — you care about outcomes, not activity
  • Operate with high agency
  • Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces
  • Can balance research exploration with engineering implementation
  • Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
  • Are comfortable with uncertainty and adapt quickly as the landscape shifts
  • Have strong software engineering skills and can build robust infrastructure
  • Enjoy pair programming (we love to pair!)
  • Have industry experience with large language model training, fine-tuning or evaluation
  • Have industry experience building RL environments, simulation systems, or large-scale ML infrastructure
  • Senior experience in a relevant technical field even if transitioning domains
  • Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems
  • Published influential work in relevant ML areas

Responsibilities

  • Build the next generation of agentic environments
  • Build rigorous evaluations that measure real capability
  • Collaborate across research and infrastructure teams to ship environments into production training
  • Debug and iterate rapidly across research and production ML stacks
  • Contribute to research culture through technical discussions and collaborative problem-solving

Technologies

reinforcement learninglarge language model trainingfine-tuningevaluationsandboxingcontainerizationVM infrastructuredistributed systems

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