Research Engineer, Computer Use
Anthropic
| Company | Anthropic |
| Category | Engineering |
| Location | San Francisco |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 30 Jun 2026 |
| Last verified | 31 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
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 focuses on teaching Claude to see, use, and understand computer interfaces. As a Research Engineer on the team, you'll work on advancing our models' ability to reliably and safely operate real software. We're looking for someone who's genuinely excited about both the research and the product sides of computer use.
Your work will translate directly into model improvements in our own and our customers' products. You can try Claude's computer use capabilities today through the Claude in Chrome extension and Claude Cowork.
Key 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
Minimum 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
Preferred Qualifications:
Experience training models for computer use or other agentic capabilities
Experience with reinforcement learning, particularly in long-horizon or sparse-reward settings
Familiarity with multimodal model training
Experience building evaluations or benchmarks for agentic systems
Experience building reinforcement learning environments, simulation systems, or large-scale ML infrastructure
Experience working closely with product teams to drive model improvements
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous socia