AI Tooling Productivity Engineer for Internal Dev

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health benefits

Dental benefits

Vision benefits

Unlimited PTO

Paid parental leave

Relocation assistance

Job summary

Thinking Machines Lab Inc. in San Francisco is hiring a developer productivity engineer to advance how we build software safely and quickly, with a focus on AI tools and coding agents.

You’ll partner with platform, security, and product engineers to standardize tools (e.g., Claude Code, Cursor, Codex), create secure agent sandboxes, and enable researchers to optimize their AI-enabled workflows while maintaining code quality. The role also involves tracking usage and costs to improve efficiency.

Qualifications

• Bachelor’s degree or equivalent industry experience in computer science, engineering, or similar.
• Experience developing productivity tools and best practices for large codebases.
• Ability to communicate clearly and work with researchers to build and manage a variety of internal tools.

Responsibilities

• Enable researchers and engineers to leverage AI to improve coding productivity without compromising code quality.
• Standardize AI coding tools such as Claude Code, Cursor, and Codex; configure and maintain org-wide settings.
• Build secure, reproducible agent sandboxes for remote dev and CI testing.
• Set up golden-path dev environments and guardrails for secrets/PII.
• Help contributors develop their personalized AI-enabled workflows.
• Track tool usage, reliability, and cost.

Skills

Rust

Python

Linux networking

Communication

Education

Bachelor’s degree in CS or related

Tools

Docker

Kubernetes

GitHub Actions

Buildkite

uv

Job description

Thinking Machines Lab Inc. in San Francisco is hiring a developer productivity engineer to advance how we build software safely and quickly, with a focus on AI tools and coding agents.

You’ll partner with platform, security, and product engineers to standardize tools (e.g., Claude Code, Cursor, Codex), create secure agent sandboxes, and enable researchers to optimize their AI-enabled workflows while maintaining code quality. The role also involves tracking usage and costs to improve efficiency.