User Experience Researcher

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.

UX research at Anthropic is a small team with outsized leverage. We work across products used by millions of people—and on interactions that don't have established conventions yet.

We inform critical decisions through rigorous research, with stakeholders who eagerly incorporate findings into their work. Research has a seat at the table for product strategy, and we partner closely with design, product, and engineering. We combine qualitative and quantitative methods to understand user needs in a space that's evolving faster than anyone can track.

We move fast, favor impact over polish, and increasingly build our own tools—often with AI doing the heavy lifting—to do research at the speed this environment demands.

This is a senior individual-contributor role. You'll own research for a complex, fast-moving product area where the surface is expanding quickly and the most important user questions haven't been articulated yet, let alone answered.

You won't work from a fixed research roadmap. You'll prioritize constantly—making clear, well-informed bets about what to learn next, executing on them fast, and pivoting the moment the picture changes. Our product teams are deeply technical and move on instinct; your job is to make that instinct better-calibrated without slowing it down.

Roles here are fluid. On any given week you might be the researcher, the analyst, the one framing the next product bet, or the one building a prototype because that's the fastest way to find out.

Where this role lands depends on team needs and your strengths—we'll figure that out together.

• →Set the research agenda for a product area where the strategic questions are still fuzzy. Define what we need to learn, in what order, and why—then go learn it. Don't wait to be asked
• →Design and run mixed-methods research at whatever fidelity the moment calls for. Some of it will be careful, foundational work the team references for a year. Some of it will be a finding you ship the same day the question came up
• →Keep product teams close to users in a rigorous way—building the channels, cadence, and evidence base that make user reality hard to ignore
• →Get hands-on with product data. Pull your own samples, build your own dashboards, instrument what's missing
• →Prototype when prototyping is the fastest path to an answer—a research tool, a survey pipeline, a scrappy version of a feature to put in front of users
• →Deeply leverage AI in how you work—using Claude to make yourself dramatically more efficient at analysis, synthesis, tooling, and everything in between
• →Use what you study. Dogfood our products hard enough to develop your own informed opinions, and bring that texture into your research
• →Anticipate where the product is heading before anyone asks. Show up with the user understanding the team is about to need
• →Turn what you learn into recommendations confident enough to redirect a roadmap—including recommending we kill something that isn't working
• →Build durable understanding, not just findings. The goal is that PMs, designers, and engineers internalize the user mental model well enough to make good calls when you're not in the room
• →Develop new research methods when the existing playbook doesn't fit—particularly methods that use AI to do things that weren't possible before

• Industry UX research experience, including ownership of research strategy for a product or product area
• Track record of research that shifted product direction at the strategy level, not just the feature level
• Deep mixed-methods expertise—qualitative and quantitative
• Enough data fluency to use Claude to work directly with our data warehouse—pulling your own samples, writing your own queries, running your own analyses
• Demonstrated technical range: you've built research tooling, prototyped, scripted analyses, or otherwise shown you can keep up with very technical teams without translation overhead
• Advanced, hands-on use of AI in your own research workflow—you're already taking full advantage of its capabilities to enhance how you work
• Experience embedding with engineering-led product teams that ship fast, and earning influence in that environment
• Excellent storytelling, calibrated to audience. You can discuss a finding with an exec as effectively as with an engineer

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.

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