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.
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 responsibilitiesOwn 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
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
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
Own and resolve the top reliability and robustness issues on the computer-control and browser-control product surfaces, with measurable improvement in task success rate
Take a net-new computer-use powered workflow from concept to external users, including instrumentation and a readout on usage
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.
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 social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Skills Required
- Experience building and shipping a product from zero to one
- Strong full-stack engineering skills
- Hands-on experience building with LLM APIs
- A track record of shipping to external users
Anthropic Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anthropic and has not been reviewed or approved by Anthropic.
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Strong & Reliable Incentives — Pay is positioned as top-of-market for many technical roles through a mix of high base pay, equity, and occasional bonuses/signing incentives. Benefits like substantial monthly stipends and employer-paid protections further strengthen perceived total rewards.
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Healthcare Strength — Healthcare is described as comprehensive across medical, dental, and vision, with additional mental-health support. Coverage is framed as robust for employees and dependents, which can materially increase the value of the overall package.
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Parental & Family Support — Paid parental leave is described as notably generous, alongside fertility coverage and other family-oriented supports. These elements broaden the rewards package beyond cash compensation and can improve retention for caregivers.
Anthropic Insights
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
