Applied Data Researcher

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!

Figma is building the system of record for digital product development: collaborative tools with natural interfaces that bring PMs, developers, QA, and designers together. That creates new-to-the-world problems in how we build AI products, understand how people use them, and evaluate what makes them useful. You'll work with a unique, petabyte-scale dataset with rich traces of collaborative design and product building to tackle those problems. We're looking for an Applied Data Researcher who combines scientific depth with a proactive builder mindset. Reporting directly to our Head of Data, with regular visibility with C-level executives, you'll identify and vet opportunities, lead investigations into critical questions, and build useful Data products, tools, and prototypes. You'll work across Data Science, Engineering, Product, Design, and AI Research, building directly and providing technical direction to teams of other scientists as efforts grow.

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:

• Identify and vet consequential opportunities across Data products and tools, AI measurement and evaluation, and applied data capabilities; lead investigations and shape priorities with partners

• Develop and validate methods for evaluating generative AI and agentic workflows, including representative datasets, baselines, rubrics, and evals; investigate uncertainty, bias, failure modes, and real user outcomes

• Personally write code and build or adapt LLM-powered tools, agentic workflows, models, prototypes, and demos that test scientific, product, and tool hypotheses, using AI-assisted coding and analysis in your work

• Provide technical direction and oversight for larger projects, delegate work to teams of senior and staff scientists, and mentor scientists and engineers while maintaining a substantial personal contribution to code and methods

• Independently drive delivery or partner with engineering to turn useful prototypes into maintained systems, balancing quality, latency, cost, reliability, and iteration speed

• Establish reusable methods, tools, and technical standards; iterate with users, drive adoption across teams, and use evidence to improve an approach or recommend a change in direction

• Communicate findings, recommendations, limitations, and tradeoffs clearly to partners and C-level executives, connecting scientific and technical work to important product and company decisions

We'd love to hear from you if you have:

• Deep expertise in machine learning, statistics, causal inference, or a related quantitative field, plus hands-on experience in generative AI evaluation and measurement, including evals, labeling, and agentic workflows

• Fluent programming skills in Python or a comparable language, plus SQL, and a record of independently building, debugging, testing, and reviewing useful software, including working effectively with AI-assisted development tools

• A proactive builder mindset and end-to-end ownership: experience identifying important opportunities and carrying ambiguous work through method development, implementation, rigorous evaluation, and sustained use by others

• A sustained record of company-, organization-, or cross-pillar scientific impact, including shaping technical direction, overseeing larger projects, and leading other senior scientists through influence and delegation

• Strong judgment, independence, and collaborative communication, including experience making recommendations to C-level executives, explaining uncertainty and tradeoffs, and aligning partners on priorities and decisions

While it's not required, it's an added plus if you also have:

• Experience with human labeling, automated judge validation, conversational data, or multimodal AI and machine learning

• Experience building interactive applications, lightweight inter