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Research Engineer, Computer Use

Anthropic· AI Research & Engineering
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Hybrid (San Francisco, CA), Hybrid (New York City, NY), Hybrid (Seattle, WA)$500k - $850kPosted 3mo ago

About the role

Research Engineer focused on teaching Claude to see, use, and understand computer interfaces, translating research into production improvements for computer use capabilities.

Responsibilities

  • Design and run experiments to improve Claude's perception and agentic capabilities
  • Develop robust, reliable evaluation frameworks for measuring our models' ability to complete complex computer tasks
  • Build and improve computer use and vision reinforcement learning training environments
  • Create pipelines and tools to test and validate complex RL environments
  • Collaborate with teams across the model training and infrastructure stack to improve our production training setup
  • Partner with product teams to bring research advances into production

Qualifications

  • Software engineering experience and proficiency in Python
  • Experience training, fine-tuning, or evaluating machine learning models
  • Strong communication skills and a collaborative working style
  • Care about the societal impacts and safety of your work

Skills

Required:PythonMachine learning model training/fine-tuning/evaluationCommunication and collaborationSocietal impacts and safety awareness
Nice to have:Computer use / agentic capabilities (preferred)Reinforcement learning (preferred)Multimodal model training (preferred)Evaluation/benchmarks for agentic systems (preferred)RL environments / simulation / large-scale ML infra (preferred)Product collaboration (preferred)

Benefits

  • Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and an office space for collaboration
Last updated Jul 8, 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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