Data Platform Engineer

Figma Figma · Enterprise · Canada +1 · Engineering

Figma is seeking a Data Platform Engineer to build foundational systems for AI-driven products and data experiences. The role involves working at the intersection of data, infrastructure, and machine learning to create scalable systems that empower Data Science, unlock AI capabilities, and integrate data and models into the product experience. The engineer will lead work on Figma's AI data agent, own and evolve the ML and data platform (including model serving, feature pipelines, orchestration, CI/CD, and monitoring), build product-facing data systems, ship platform tooling for ML practitioners, and design infrastructure for AI-assisted interfaces.

What you'd actually do

  1. Lead work on Figma’s AI data agent to enable self-serve analytics by owning the data-agent layer, building prompt-processing pipelines, instrumenting interactions, and delivering prompt-based usage analytics
  2. Own and evolve Figma’s ML and data platform, including model serving, feature pipelines, workflow orchestration, CI/CD for models, and production monitoring
  3. Build product-facing data systems and data products so models and data become first-class components of Figma’s product experience
  4. Ship platform tooling that enables Data Science teams to deploy, iterate on, and operate models effectively, including feature stores, rollout systems, and observability
  5. Design and scale infrastructure for AI-assisted and natural language interfaces to data, unlocking self-serve analytics across the company

Skills

Required

  • 5+ years of experience in data platform, infrastructure, or machine learning engineering
  • 1+ years working on AI or ML systems
  • Experience building and operating end-to-end ML systems in production (training, evaluation, deployment, monitoring)
  • Strong programming skills in Python or a similar language
  • Proven ability to build reliable, scalable systems and services
  • Experience designing ML infrastructure (model serving, feature pipelines, workflow orchestration) and scalable system architectures
  • Proven ability to work cross-functionally and drive projects across Data Science, Engineering, Infrastructure, and Product
  • Experience in data modeling and data product design

Nice to have

  • Experience with ML platform tooling such as MLflow, Kubeflow, feature stores, or CI/CD for ML workflows
  • Familiarity with LLMs, RAG systems, prompt processing, or AI-native infrastructure
  • Experience building self-serve analytics platforms or internal data tools
  • Experience with modern data stack technologies such as Snowflake, dbt, or Dagster, and cloud platforms such as AWS
  • A strong product mindset and interest in connecting platform work to user and business impact

What the JD emphasized

  • AI or ML systems
  • end-to-end ML systems in production
  • ML infrastructure
  • LLMs, RAG systems, prompt processing, or AI-native infrastructure

Other signals

  • ML and data platform
  • production-grade AI systems
  • AI-assisted and natural language interfaces to data