Data Platform Engineer

The role

This individual contributor role focuses on designing and operating scalable data platform systems that enable machine learning and AI capabilities across Figma. The engineer will work at the intersection of data infrastructure, ML systems, and product experience, reporting to the Data Engineering team and collaborating across Data Science, AI/ML, Infrastructure, and Product organizations.

What you'd do

• Lead development of Figma's AI data agent for self-serve analytics, including data-agent layer and prompt-processing pipelines
• Own and maintain ML and data platform components such as model serving, feature pipelines, and workflow orchestration
• Build product-integrated data systems that embed models and analytics as core product features
• Create platform tooling enabling Data Science teams to deploy, monitor, and iterate on models efficiently
• Design and scale infrastructure supporting AI-assisted and natural language interfaces for analytics
• Drive cross-functional initiatives establishing data contracts, SLAs, and system design standards
• Enhance developer experience for ML and data practitioners through abstractions and tooling

What they're looking for

• Minimum five years in data platform, infrastructure, or machine learning engineering roles
• At least one year of hands-on experience with AI or ML systems
• Demonstrated ability building and operating complete ML systems from training through production monitoring
• Strong Python programming skills with proven track record of reliable, scalable systems
• Experience designing ML infrastructure including model serving, feature pipelines, and orchestration
• Cross-functional collaboration experience across Data Science, Engineering, Infrastructure, and Product teams
• Knowledge of data modeling and data product design principles

Nice to have

• Experience with ML platform tools like MLflow, Kubeflow, feature stores, or ML-specific CI/CD
• Familiarity with LLMs, RAG systems, prompt processing, or AI-native infrastructure
• Background building self-serve analytics platforms or internal data tools
• Experience with modern data stack technologies such as Snowflake, dbt, Dagster, or AWS
• Product-oriented mindset connecting platform work to user and business outcomes