User Researcher

Notion is the collaborative AI workspace where teams and agents think together. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.

We’re seeking an experienced UX Researcher to define and scale how we evaluate Notion’s AI-powered experiences—focusing on what “good” looks like not only for model output quality, but for the end-to-end product experience where people discover, set goals, delegate work, review results, and build trust over time with AI.

This role sits at the intersection of research craft and evaluation operations: you’ll run studies that uncover user mental models, expectations, and failure/recovery behaviors, then translate those insights into reusable rubrics, workflows, and measurement approaches that product, design, engineering, and data science can apply consistently.

This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person.

• Define what “good” looks like (frameworks & rubrics): Establish clear, reusable evaluation criteria that reflect real user expectations—helpfulness, trust, tone, control, and transparency. 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.
• 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.
• 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.
• 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.

• Ability to operationalize insight into measurement: comfortable turning “soft” user expectations (trust, tone, usefulness, clarity) into concrete rubrics, scoring guidelines, and observable metrics.
• AI fluency and systems thinking: curious and hands-on with AI products, can reason about how model behavior, uncertainty, and system constraints shape user experience. Experience evaluating AI-enabled products (LLMs, agents, generative UI/workflow automation) and working with Data Science/ML partners.
• Clear communication and impact orientation: align diverse partners around shared definitions of quality and create artifacts that enable teams to act consistently.
• Strong UX research craft (quant + qual): choose the right methods for the question—interviews, benchmarking, surveys, experiments—and synthesize into actionable guidance.
• Pragmatism in fast-moving environments: prioritize ruthlessly, work through ambiguity, balance scrappy iteration with deep dives.
• 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.