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  3. Director, Research - AI Evals
Illustration - Director, Research - AI Evals

Director, Research - AI Evals

Figma
Figma
San Francisco, CA • New York, NY • United States
Jul 13, 2026
Salary not listed

At a glance

  • Full time

Job Description

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us!

The Figma Research team is hiring an Director, Research - AI Evals to own how we measure the quality of Figma's AI-powered experiences. As Figma ships more AI capabilities across our products, the question "is this really good?" has never mattered more — and answering it rigorously is what this role exists to do. You'll define what "good" means for our AI features, build the frameworks and quality bars to measure it, and turn that into trusted signal that product teams rely on to decide what to ship.

The ideal candidate brings deep, hands-on experience evaluating AI/LLM-powered products — blending human evaluation with automated, model-based approaches — along with the product instinct and communication skills to make evaluation genuinely useful. Partnering with Product, Design, Engineering, and Data Science, you'll sit upstream of nearly every AI shipping decision at Figma and directly shape the quality of features used by millions of people.

This is a full time role that can be held from one of our US hubs or remotely in the United States.

What you'll do at Figma:

  • Own AI evaluation methods and operations for Figma's AI-powered experiences — define quality dimensions, design how we measure them, and turn results into decision-ready signal
  • Build and maintain evaluation frameworks, rubrics, golden datasets, and quality bars, combining human evaluation with automated/model-based approaches (e.g., LLM-as-judge) where appropriate
  • Partner with engineering to stand up repeatable, reproducible evaluation pipelines and regression testing, so evaluation is a routine part of how AI features are built and shipped

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