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 interfaces, APIs, or reusable open-source tools
- Cloud and data infrastructure experience; familiarity with Snowflake, AWS, Dagster, dbt, or Hex is useful, but no particular vendor or framework is required
- An MS or PhD in machine learning, computer science, statistics, or a similar quantitative discipline; graduate research, publications, or equivalent applied depth
At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Pay Transparency DisclosureJob level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location.
Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer-provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law.
Annual Base Salary Range:
$235,000-$376,000 USD
