Product Designer, Evals & Prompts

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.

Prompts spec out what Claude does. Evals measure whether it did. The Product Prompt and Eval Design team does both for product design: the system prompt that greets a new user, the tool descriptions that decide whether Claude searches, the instructions that keep a slide deck from tipping into slop, and the evals that test all of it. The point is to keep the model and the product aligned with what users expect, what the product strategy calls for, and what safety requires, on every surface and through every model launch.

This role is a foundational member of that work on the eval side: building the evals that check the prompts, the harness that runs them, and the tools that let designers do this work themselves. It sits on the Product Prompt and Eval Design team in Product Design, works day to day with the team's surface owners and the engineers in each product team, and pairs per surface with the prompt engineering team at model releases.

Key responsibilities

Minimum qualifications

• Write and revise the prompts behind Claude's tools, features, and behaviors on a product surface; test the surface, turn findings into prompt fixes, ship them, and confirm the prompt users get is the one intended
• Build the graders that prove a prompt fix and rerun on the next model; turn designers' hand-run rubrics into automated evals, then read transcripts for what the eval missed
• Build visual, low-code eval tools designers can use without an engineer: assemble a comparison set from real transcripts, turn a plain-English rubric into a grader, compare prompt variants across models side by side, and read results in the tool rather than a notebook
• Watch designers use those tools and make them simpler
• Support model releases: test each surface against the new model, write prompt fixes and migrations, and write prompts for features launching with it, so the surface owner's call has numbers behind it
• Stand up and scale the eval harness: build the test environment that exercises our 50 to 100 tools with trustworthy settings, keep evals green across models, and call whether a regression is the harness or the model
• Package what prompting can't fix for training, with the eval attached: graders for crisp behaviors, human-feedback questions and good/bad pairs for fuzzy ones like writing quality
• Production-quality Python
• Experience building and maintaining evaluation pipelines for LLM products: graders, rubrics, comparison sets, regression suites, and the plumbing that runs them across models
• Experience building internal tools with a real interface for people who do not write code
• Experience standing up test harnesses, sandboxing tool calls, and pinning the settings that make runs comparable
• Experience shipping prompts, or working closely with people who do, and understanding why a prompt that works on one model fails on the next
• Reads transcripts, not only scores

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.

• Has worked inside a model-launch cycle
• A/B testing experience and the ability to connect offline evals to online outcomes
• Front-end or notebook-to-app experience, and opinions about what makes an eval result legible at a glance
• Has turned product rubrics into training signal: graders, human-feedback questions, or preference pairs

$305,000—$385,000 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 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.