Anthropic
Product Engineer, Computer Use
About this role
Anthropic seeks a Product Engineer to lead end-to-end development of computer-use and browser-control products that enable Claude to interact with digital interfaces. You'll build full-stack solutions bridging research and real-world applications, working directly with researchers and users in a fast-moving AI environment.
What you'll do
- Own complete product delivery lifecycle for computer-use and browser-control features—from scoping through shipping, measurement, and iteration
- Debug and fix reliability issues in the agent harness that impact real-world usage
- Collaborate with computer-use researchers to translate capabilities into products
- Partner with the Claude Cowork team on integrations and knowledge-worker workflows
- Instrument products and analyze usage data to guide prioritization
- Convert user pain points into concrete, shippable features
What they're looking for
- Full-stack web development (frontend and backend)
- LLM API integration and prompting
- Agent frameworks and design
- Product shipping and user iteration
- Browser or desktop automation
- Data analysis and SQL
- Product design and UI/UX
- Machine learning evaluation and quality assurance
Benefits
- Annual salary: $300,000–$320,000 USD
- Hybrid work model with 25% minimum office time
- Work directly with AI researchers and product teams
- Influence cutting-edge AI agent capabilities
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Tell us about a product you built from zero to one with end-to-end ownership. How did you scope it, ship it, and iterate based on user feedback?
- Describe your experience building with LLM APIs or agent frameworks. What challenges did you encounter and how did you solve them?