Data Platform Engineer

Role Overview

Join Figma’s Data Engineering team as a Data Platform Engineer. You’ll help build the foundational ML and data platform systems that power AI-driven products and self-serve analytics, working at the intersection of data, infrastructure, and machine learning.

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
  • production monitoring
• Build product-facing data systems and data products so models and data become first-class parts of Figma’s product experience.
• Ship platform tooling that helps Data Science teams deploy, iterate, and operate models effectively (e.g., feature stores, rollout systems, observability).
• Design and scale infrastructure for AI-assisted and natural language interfaces to data.
• Drive cross-functional platform initiatives by aligning data contracts, SLAs, and system design across Data Science, AI/ML, Infrastructure, and Product.
• Improve the developer experience for ML and data practitioners via abstractions, tooling, and platform capabilities.

Requirements

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

Nice to Haves

• Experience with ML platform tooling such as MLflow, Kubeflow, feature stores, or CI/CD for ML workflows.
• Familiarity with LLMs, RAG, 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.
• Product mindset and interest in connecting platform work to user and business impact.