Staff Software Engineer, Internal Products

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

As Anthropic grows, more of the work our own teams do every day runs through internal software, and that software has to keep up. We are building that software, and we are building it Claude-first: Claude should take on as much of the repetitive, multi-step work as it safely can, and people decide what needs a human.

We’re looking for Staff+ full-stack engineers to drive this work. You’ll build across the stack: the products people use and the agents that work alongside them. You’ll work directly with the teams who rely on what you build. Many of these workflows write to business-critical data, so correctness matters as much as speed.

This is a greenfield opportunity: you’ll shape the underlying systems, ship agents that take real work off people’s plates, and design how people and agents get the right context to make good decisions.

Key responsibilities

• Design and ship internal products end to end, from the data model to the UI, for workflows that teams across Anthropic depend on.

• Put Claude to work on real tasks, such as gathering the context a decision needs, drafting the next step of a multi-step process or keeping records consistent across the systems teams already use, and decide where an agent should act and where a well-designed UI is the better answer.

• Make agents safe to trust with real work: scoped access to data, guardrails, evals, audit trails, and clear points where a person confirms before anything happens.

• Build one set of shared capabilities that people and agents both use, exposed as APIs and MCP tools, so the same rules and approvals apply to whoever makes the call.

• Treat speed and polish as product requirements. Spend time with the people who use what you build, find where their work slows down, and remove those steps.

• Build for correctness. When a workflow writes to business-critical data, a mistake travels into other systems, so you’ll know where idempotency, reconciliation and audit trails matter and where they don’t.

• Integrate with the third-party systems teams already rely on, and build the layer on top of them where people and agents get their work done.

• Set technical direction for a product area over multiple quarters, lead engineers across teams through large, ambiguous projects, and make the engineers around you better through design reviews, code review and mentorship.

• Operate what you ship: own the reliability of the workflows people depend on and be on call for them.

Minimum qualifications

• Experience shipping production software as a backend-leaning full-stack engineer, comfortable designing a data model, an API and the UI on top of them.

• Experience building software on LLMs or agents, and a point of view on when an agent should act on its own and when it should ask first.

• Experience leading the design and delivery of complex, user-facing products across the full stack, through requirements that shifted while you built.

• Experience designing for correctness and data integrity from the start, in systems where errors are costly and hard to undo.

• Experience working closely with non-engineering partners, turning ambiguous needs into clear requirements and reaching decisions together.

Preferred qualifications

• Experience building internal tools or platforms that other teams rely on. A plus, not a requirement; strong product generalists are just as welcome.

• Experience taking an agent into production for a high-stakes workflow, including the evals and guardrails that made it safe to ship.

• Experience building a zero-to-one product, or rebuilding one that people already depended on every day.

• Experience building on top of third-party platforms through their APIs, and knowing when to extend a system and when to build your own.

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.

For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.

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.

How we're different

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.