Product Engineer, Computer Use

Anthropic Anthropic · AI Frontier · New York, NY +2 · AI Research & Engineering

Product Engineer role focused on building and shipping AI-powered computer-use and browser-control product surfaces. This involves full-stack development, agent harness, and working with LLM APIs and agent frameworks. The role requires end-to-end ownership and iteration based on user feedback, with a focus on reliability and robustness of the agent harness.

What you'd actually do

  1. Own end-to-end delivery of computer-use and browser-control product surfaces: scope, build, ship, measure, and iterate
  2. Diagnose and resolve reliability and robustness issues in the computer-use agent harness that block real-world usage
  3. Partner with computer-use researchers
  4. Partner with the Claude Cowork team on shared surfaces, integrations, and knowledge-worker workflows
  5. Instrument products and use usage data to drive prioritization and measure progress

Skills

Required

  • Experience building and shipping a product from zero to one with end-to-end ownership
  • Strong full-stack engineering skills, including production web frontend and backend development
  • Hands-on experience building with LLM APIs, prompting, or agent frameworks
  • A track record of shipping to external users and iterating based on their feedback

Nice to have

  • Strong product design instincts and the ability to produce a clean, usable interface without a dedicated designer
  • Experience with browser automation, desktop automation, or robotic process automation systems
  • Experience building evals or quality harnesses for machine learning systems
  • Comfort with lightweight data analysis, such as SQL, notebooks, and defining and tracking product metrics
  • Experience designing agent loops, tool integrations, or guardrails for LLM-based systems

What the JD emphasized

  • end-to-end ownership
  • reliability and robustness issues
  • external users
  • agent loops
  • tool integrations
  • guardrails

Other signals

  • Own end-to-end delivery of computer-use and browser-control product surfaces
  • Build across the full stack, from the user interface to the agent runtime to the backend services behind it
  • Hands-on experience building with LLM APIs, prompting, or agent frameworks
  • Experience building evals or quality harnesses for machine learning systems
  • Experience designing agent loops, tool integrations, or guardrails for LLM-based systems