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Staff+ Research Scientist, Multi-Agent

Anthropic· AI Research & Engineering
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Hybrid (San Francisco, CA), Hybrid (New York City, NY), Hybrid (Seattle, WA)$500k - $850kFull-timePosted 11mo ago

About the role

Research scientist role focused on training and evaluating large-scale multi-agent systems to improve Claude's performance and safety in agentic tasks; emphasis on environments, agent harness configurations, rigorous benchmarks, and collaboration with product teams.

Responsibilities

  • Help create and optimize environments and data for model training that maximize Claude’s performance or ease of use on agentic tasks
  • Ideate, develop, and compare the performance of different agent harness configurations (eg memory, context management, communication architectures for agents)
  • Design and implement rigorous quantitative benchmarks for large scale agentic tasks
  • Work with our product org to find solutions to our most vexing challenges in applying agents to our products

Qualifications

  • Have experience with large-scale RL on language models
  • Have experience training multi-agent systems
  • Enjoy going deeply into the roots of a problem and understanding its foundations, rather than its surface
  • Have good communication skills and an interest in working with other researchers on difficult tasks
  • Have a passion for making powerful technology safe and societally beneficial
  • Are excited for a mission-driven org with fast-paced, impactful work

Skills

Required:Large-scale RL with language modelsMulti-agent system trainingBenchmark design and evaluationCommunication and collaboration

Benefits

  • Competitive compensation and benefits
  • Optional equity donation matching
  • Generous vacation and parental leave
  • Flexible working hours
  • Lovely office space for collaboration
Last updated Sep 30, 2026. Interested? Apply on the company site.

About Anthropic

building reliable, interpretable, and steerable AI systems

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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