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’re looking for a full stack engineer to build and ship products from prototype to scale and to maintain tools that accelerate research and product teams. You’ll work across frontend and backend components, and contribute to reliability, observability, and security in production.
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- Prototype and build new APIs and product backends in Python and Rust.
- Launch new products and UX with React and TypeScript where needed.
- Improve developer experience for local dev, deployment, testing, and iteration speed.
- Improve system reliability, observability, and security across production environments; participate in on‑call.
Minimum qualifications:
- Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
- Proficiency in at least one backend language (we use Python or Rust).
- Some familiarity with ReactJS, TypeScript or mobile platforms.
- Comfort operating across the stack and owning projects end-to-end.
- Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
- A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
Preferred qualifications — we encourage you to apply if you meet some but not all of these:
- Experience designing and maintaining backend APIs at scale.
- Experience building tooling or products for LLMs or other systems that scale to a large number of users.
- Ability to build high‑quality, production-level UIs from prototype to polish.
- Familiarity with NodeJS, Python, and/or Rust.
- Experience building AI products or other products that scale to a large number of users.
- 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.
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