The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
About the RoleThis role is responsible for building and strengthening the engineering foundations that our post-training research teams depend on. You'll embed within a research team, and build or improve the systems required for the team to succeed. Our research teams are small, and the role carries a corresponding degree of autonomy and responsibility.
What You’ll DoEmbed within a research team to build, harden, and improve the systems and infrastructure required for the team to succeed.
Design, build, and operate infrastructure research teams depend on, including RL training systems, sandboxing, data pipelines, and agent scaffolding.
Minimum qualifications:
Experience leading projects end to end, working in large fast-moving codebases, and writing code others have depended and built on.
Strong proficiency in Python and strong engineering fundamentals, with experience debugging systems that fail intermittently and at scale.
Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
Clarity in communication, an ability to explain complex technical concepts in writing.
Strong autonomous drive to progress towards the team’s goals with an ownership mindset.
Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but preferably some:
Experience building or iterating with sandboxed or containerized execution environments at a large scale.
Experience in a role where the team's priorities set yours, and a track record of success in such a role.
Familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).
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 350000-475000 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.
Skills Required
- Experience leading projects end to end
- Experience working in large, fast-moving codebases
- Experience writing code that others depend on and build upon
- Strong proficiency in Python
- Strong engineering fundamentals
- Experience debugging systems that fail intermittently and at scale
- Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline
- Strong theoretical and empirical grounding
- Clear written communication and ability to explain complex technical concepts
- Strong autonomous drive and ownership mindset
- Experience building or iterating with sandboxed or containerized execution environments at large scale
- Experience succeeding in a role where team priorities set individual priorities
- Familiarity with at least one deep learning framework, such as PyTorch, TensorFlow, or JAX
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.
