Software Engineer, Machine Learning

Figma Figma · Enterprise · Canada +1 · Engineering

Figma is seeking an experienced Machine Learning / AI Engineer to join their AI team, focusing on applied ML, infrastructure, and product innovation. The role involves designing, building, and productionizing ML models for Search, Discovery, Ranking, RAG, and generative AI features, as well as building scalable data pipelines and collaborating with researchers and product engineers to deliver AI-driven features and infrastructure. The position requires strong experience in end-to-end ML model development, Python, and ML libraries, with a focus on scalable data and annotation pipelines and evaluation systems.

What you'd actually do

  1. Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features.
  2. Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.
  3. Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.
  4. Work with product engineers to define and deliver impactful AI features across Figma’s platform.
  5. Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment.

Skills

Required

  • 5+ years of industry experience in software engineering
  • 3+ years focused on applied machine learning or AI
  • end-to-end ML model development
  • training
  • evaluation
  • deployment
  • monitoring
  • Python
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • Spark MLlib
  • XGBoost
  • designing and building scalable data and annotation pipelines
  • evaluation systems for AI model quality

Nice to have

  • search relevance
  • ranking
  • NLP
  • RAG systems
  • AI infrastructure
  • MLOps
  • observability
  • CI/CD
  • automation for ML workflows
  • creative or design-focused ML applications
  • C++
  • Go
  • product mindset
  • collaboration and communication skills

What the JD emphasized

  • productionize ML models
  • scalable data pipelines
  • evaluation datasets
  • AI model quality
  • end-to-end ML model development
  • training, evaluation, deployment, and monitoring
  • scalable data and annotation pipelines
  • evaluation systems

Other signals

  • productionize ML models
  • scalable data pipelines
  • ML-driven features
  • AI features across Figma’s platform
  • systems for training, inference, monitoring, and deployment