Data Platform Engineer

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