Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
We are hiring a developer productivity engineer to advance how we build software internally: safely, quickly, and with delight. The main focus are AI tools and coding agents. You’ll partner with platform, security, and product engineers to build state-of-the-art tooling for AI-assisted software development, and make our inner loop dramatically faster.
The scope of this role includes both setting up company-wide platforms and working with developers to accelerate their individual workflows.
Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.
What You’ll Do- Enable our 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. You will help configure, harden, and maintain the best tools, integrating org-wide configurations with individual preferences.
- Build secure, reproducible agent sandboxes for remote dev & CI testing.
- Set up golden-path dev environments and guardrails for secrets/PII.
- Help individual contributors develop their personalized AI-enabled workflow.
- Track tool usage, reliability, and cost.
Minimum 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.
Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:
- Hands-on experience with container platforms (e.g. Docker/Kubernetes), modern CI (GitHub Actions/Buildkite), and package management tools (uv).
- Practical experience with AI coding tools and model APIs (e.g. OSS via vLLM / SGLang / TGI).
- Solid Linux/networking fundamentals; comfort with secrets management and safe egress.
- Proficiency in systems programming languages (e.g. Rust) and scripting languages (e.g. Python).
- Location: This role is based in San Francisco, California.
- Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
- Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
- Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
Top Skills
What We Do
Thinking Machines Lab is an artificial intelligence research and product company. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.
While AI capabilities have advanced dramatically, key gaps remain. The scientific community's understanding of frontier AI systems lags behind rapidly advancing capabilities. Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively. And, despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable.
We are scientists, engineers, and builders who've created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
