Product Engineer, Computer Use

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

The Computer Use team teaches Claude to see and operate computer interfaces, and builds the agent harness and end-user products that turn that capability into real tools. The team sits inside Anthropic's research organization and closes the loop between product and model.

As a Product Engineer on this team, you'll own end-to-end delivery of our computer-use and browser-control product surfaces. You'll build across the full stack, from the user interface to the agent runtime to the backend services behind it. You'll work directly alongside researchers, with no layers between you and the model or the user.

This is a dynamic role in which priorities evolve frequently. Success depends on a high tolerance for ambiguity, the adaptability to shift focus as needs change, and the agility and discernment to continuously prioritize the highest impact work.

Key responsibilities

Own end-to-end delivery of computer-use and browser-control product surfaces: scope, build, ship, measure, and iterate

Diagnose and resolve reliability and robustness issues in the computer-use agent harness that block real-world usage

Partner with computer-use researchers

Partner with the Claude Cowork team on shared surfaces, integrations, and knowledge-worker workflows

Instrument products and use usage data to drive prioritization and measure progress

Translate fuzzy user pain points into concrete, shippable features for knowledge workers

Minimum qualifications

Experience building and shipping a product from zero to one with end-to-end ownership, as a founding or early engineer at a startup or with equivalent ownership inside a larger company

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

Preferred qualifications

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

Representative