User Researcher, AI Evaluations

Define what “good” looks like (frameworks & rubrics): Establish clear, reusable evaluation criteria that reflect real user expectations—helpfulness, trust, tone, control, and transparency. You’ll translate qualitative insight into scoring guidance that can be applied consistently across teams and over time.

Run recurring evals (longitudinal & feature-specific): Run recurring longitudinal and feature-specific surveys and studies to measure experience quality over time against defined rubrics. Lead qualitative studies, side-by-side comparisons, and human-in-the-loop evaluation efforts to deepen understanding of where experiences break down and how they can improve. You’ll help teams spot regressions, benchmark improvements, and understand when expectations shift.

Anchor evaluation in real workflows (context > isolated feedback): Ensure evals reflect jobs-to-be-done, user intent, and the full interaction journey (goal setting, delegation, review, iteration), not just decontextualized thumbs up/down. You’ll help teams understand who is evaluating, what they’re trying to do, and why outputs succeed or fail.

Identify failure modes & recovery behavior (guardrails): Uncover breakdowns, regressions, and edge cases across the system—from model behavior to UI and integrations—and study how people notice issues, correct them, and continue their work. You’ll turn these insights into actionable guidance for guardrails, fixes, and prioritization.

Operationalize evaluation with partners (process & tooling): Collaborate closely with Product, Design, Engineering, and Data Science to align on target use cases and build scalable evaluation loops (human-in-the-loop review, longitudinal studies, and calibration of automated/LLM-judge approaches against human judgment).

Ability to operationalize insight into measurement: You’re comfortable turning “soft” user expectations (trust, tone, usefulness, clarity) into concrete rubrics, scoring guidelines, and observable metrics.

AI fluency and systems thinking: You’re curious and hands-on with AI products, and can reason about how model behavior, uncertainty, and system constraints shape user experience. You also have experience evaluating AI-enabled products (LLMs, agents, generative UI/workflow automation) and working with Data Science/ML partners on measurement strategy and evaluation tooling.

Clear communication and impact orientation: You can align diverse partners around shared definitions of quality and create artifacts that enable teams to act consistently. You tailor storytelling to different audiences, connect research to business outcomes, and drive follow-through so insights translate into product change.

Strong UX research craft (quant + qual): You can choose the right methods for the question— interviews, benchmarking, surveys, experiments—and synthesize into actionable guidance. You also can prioritize ruthlessly, work through ambiguity, and balance scrappy iteration with deep dives when needed.

Pragmatism in fast-moving environments: You can prioritize ruthlessly, work through ambiguity, and balance scrappy iteration with deep dives when needed.

Experience: 5+ years doing UX research in industry

Familiarity with LLM-as-judge methods, prompt design for evaluators, or “golden dataset” creation

Experience using AI research tooling for rapid synthesis and communication (e.g., Dovetail, Listen Labs, Maze, Outset, etc.), as well as AI observability tooling like Braintrust

Experience using data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab, etc.)

Master’s or PhD in HCI, Psychology, Behavioral Science, Anthropology, Sociology, or a related field

You’re familiar with the work of computing heroes like Douglas Engelbart, Alan Kay, Bret Victor, etc. — and understand why we're big fans.

Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role’s scope and complexity, and the candidate’s experience and expertise, and may vary from the range provided below. For roles based in San Francisco or New York City, the estimated base salary range for this role is $196,000-$230,000 per year.

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You don’t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement — when that’s the case, we'll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on.

We hire talented people from a wide range of backgrounds. If you’re excited about this role but don’t meet every bullet, we still encourage you to apply. Notion is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Consistent with applicable law, we will consider for employment qualified applicants with arrest and conviction records. Notion provides reasonable accommodations during the application process; if you need one, please let your recruiter know.

Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.