Infrastructure Engineer - Data Platform

TypeSafe AI•San Francisco, CA

1d•Onsite

About The Position

As a data infrastructure engineer, you will build the internal data platform and tooling that powers TypeSafe’s model training, evaluation, and experimentation. The systems you build will sit on the critical path of how we acquire data, evaluate model behavior, and improve our models through tight iteration loops. This data infrastructure is where much of the leverage in modern AI systems comes from! This role focuses on building the developer-facing systems that make working with large datasets and model outputs easy, safe, and scalable. Our tech stack is primarily Python. We also use TypeScript, Next.js, and Tailwind CSS for frontend, with Kubernetes for orchestration. We empower developers to use any tooling they find helpful for getting their job done, including Claude Code and Cursor.

Requirements

Responsible, ownership-inclined, and a team player – you believe there is no such thing as “other people’s code”
Enjoy building tools and abstractions that improve developer productivity
Have experience designing data systems or internal developer platforms
Care deeply about correctness, reliability, and maintainability in systems that handle critical data
Collaborate well with others on technical and product design, advocating for what you need and adjusting to changing requirements
Comfortable working in ambiguous problem spaces and building systems from first principles
Mission aligned and excited to go all-in
Love being part of a team
5+ years of professional software engineering experience (3+ years working on backend, data infrastructure, or ML systems)
Experience designing systems that acquire, transform, and manage large datasets efficiently
Experience with large-scale data analysis and experimentation
Experience working closely with researchers or ML engineers to translate data needs into scalable systems

Nice To Haves

Previously built big things
Experienced LLMs’ capabilities and limitations from implementing them in the past

Responsibilities

Design and build internal tools for managing datasets, model outputs, and evaluation results
Create reliable systems for dataset versioning, lineage, and reproducibility
Develop abstractions and APIs that allow research and product teams to interact with data without needing to understand underlying infrastructure
Build tooling that accelerates data acquisition, labeling, curation, and analysis
Create observability and debugging tools that make it easy to understand how data flows through training and evaluation systems
Collaborate closely with research, product, and infrastructure teams to ensure data systems support rapid experimentation while maintaining correctness and reliability