Product Designer, Evals & Prompts

About the role

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

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

Minimum qualifications

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

Preferred qualifications

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

Cares how Claude behaves for the people using it, not only whether the metric moved

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:

$305,000—$385,000 USD