Figma is a company on a mission to make design accessible to all, seeking a Data Platform Engineer to join their Data Engineering team. The role involves building foundational systems for AI-driven products and data experiences, focusing on scalable systems that empower Data Science and enhance product experiences.
Responsibilities:
- 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
- Own and evolve Figma’s ML and data platform, including model serving, feature pipelines, workflow orchestration, CI/CD for models, and production monitoring
- Build product-facing data systems and data products so models and data become first-class components of Figma’s product experience
- Ship platform tooling that enables Data Science teams to deploy, iterate on, and operate models effectively, including feature stores, rollout systems, and observability
- Design and scale infrastructure for AI-assisted and natural language interfaces to data, unlocking self-serve analytics across the company
- Drive cross-functional platform initiatives, aligning data contracts, SLAs, and system design across Data Science, AI/ML, Infrastructure, and Product
- Improve the developer experience for ML and data practitioners through robust abstractions, tooling, and platform capabilities
Requirements:
- 5+ years of experience in data platform, infrastructure, or machine learning engineering, with at least 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, with 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, with experience in data modeling and data product design
- 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