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Infrastructure Engineer - Data Platform
TypeSafe AI
San Francisco, USA
Full-time
Senior · 5+ years experience
Onsite
USD 180000-280000 yearly / year
Discovered 1 weeks ago
PythonTypeScriptNext.jsTailwind CSSKubernetesClaude Code
Free
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Role Overview
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.
Key Skills for This Role
PythonTypeScriptNext.jsTailwind CSSKubernetesClaude Code
Full Job Posting
About the Role
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.
Responsibilities
- The role is wide ranging and you will wear many hats. Responsibilities include:
- Design and build internal tools for managing datasets, model outputs, and evaluation results
- Create reliable systems for dataset versioning, lineage, and reproducibility
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