About the Role
Figma is seeking a versatile and experienced Machine Learning / AI Engineer to join our growing AI team, working at the intersection of applied machine learning, infrastructure, and product innovation. Whether you’re building intelligent search systems, crafting scalable data pipelines, or enhancing AI-powered creativity tools, your work will drive user productivity, shape new product experiences, and advance the state of AI at Figma.
You’ll collaborate closely with engineers, researchers, designers, and product managers across multiple teams to deliver high-quality ML-driven features and infrastructure. This is a high-impact, cross-functional role where you’ll shape both foundational systems and user-facing capabilities.
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:
- Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features.
- Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.
- Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.
- Work with product engineers to define and deliver impactful AI features across Figma’s platform.
- Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment.
- Explore new ideas at the edge of what’s technically possible and help shape the long-term AI vision at Figma.
We’d love to hear from you If you have:
- 5+ years of industry experience in software engineering, with 3+ years focused on applied machine learning or AI.
- Strong experience with end-to-end ML model development, including training, evaluation, deployment, and monitoring.
- Proficiency in Python and familiarity with ML libraries like PyTorch, TensorFlow, Scikit-learn, Spark MLlib, or XGBoost.
- Experience designing and building scalable data and annotation pipelines, as well as evaluation systems for AI model quality.
- Experience mentoring or leading others and contributing to a culture of technical excellence and innovation.
While not required, It’s an added plus if you also have:
- Familiarity with search relevance, ranking, NLP, or RAG systems.
- Experience with AI infrastructure and MLOps, including observability, CI/CD, and automation for ML workflows.
- Experience working on creative or design-focused ML applications.
- Knowledge of additional languages such as C++ or Go is a plus, but not required.
- A product mindset with the ability to tie technical work to user outcomes and business impact.
- Strong collaboration and communication skills, especially when working across functions (engineering, product, research).