Software Engineer - Machine Learning

Figma is a collaborative design and creativity platform that helps teams bring ideas to life and work together in real time. The Software Engineer - Machine Learning will build and productionize machine learning models, scalable data pipelines, and AI-powered product features while collaborating with researchers, product engineers, designers, and infrastructure teams.

Responsibilities:

• 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

Requirements:

• 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
• 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)