AI Applied Scientist
Figma · San Francisco, CA | New York City, NY | United States · Engineering
About this role
Figma is hiring a mid-level Applied Scientist in the machine learning function based in San Francisco, CA | New York City, NY | United States. The posting calls out experience with Python, Java, R, PyTorch. Compensation is listed at $153,000–$376,000 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Francisco, CA | New York City, NY | United States
- Department
- Engineering
More roles at Figma
Job description
from Figma careersFigma 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!
We’re looking for applied scientists with a Machine Learning and Artificial Intelligence background to build AI technologies and make Figma products more magical.. You will be driving fundamental and applied research in this area. You will be combining industry best practices and a first-principles approach to design and build AI/ML models and systems to improve Figma’s products.
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:
- You will be driving fundamental and applied research in AI. You will explore the boundaries of what is possible with the current technology set to build best in class models for Figma’s domains
- You will be combining industry best practices and a first-principles approach to build cutting edge Generative AI models, using techniques like Supervised Finetuning (SFT), Reinforcement Learning (RL), prompt improvements and synthetic data generation