About the role
About Thinking Machines
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 Role
At Thinking Machines we build tools that enable people to make AI their own, customizing models to serve their unique needs. This includes the ability to train model weights.
You'll lead the Fine-tuning Science team, setting the research agenda for frontier customization techniques and for Tinker, the leading post-training engine. This is a player-coach role: you'll stay hands-on in the science while growing the team, shaping its direction, and making sure findings ship into Tinker. You'll work closely with our internal research teams, contribute to open science, and engage with external users.
What You’ll Do
In this role, you'll advance the science of fine-tuning and frontier post-training techniques. You’ll:
- Set the research agenda: choose the problems, place the bets, and own the roadmap for pushing Tinker quality, efficiency, and reliability to the frontier.
- Lead and grow the team: hire, mentor, and develop researchers, and set the bar for experimental rigor and research taste.
- Stay hands-on in areas like LoRA and parameter-efficient fine-tuning and how they interact with RL and post-training.
- Improve the stability, efficiency, and reliability of large-scale fine-tuning and RL runs on Tinker.
- Represent the work externally through papers, technical blog posts, and community contributions.
Skills and Qualifications
Required qualifications:
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
- A track record of leading research – setting direction for a team or a major research effort and delivering on it.
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales.
- Clarity in communication: an ability to explain complex technical concepts in writing and to build alignment across science, systems/infra, product.
- Strong interest in our mission to enable custom models.
Preferred qualifications — we encourage you to apply if you meet some but not all of these:
- A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
- Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models.
- Experience managing or analyzing human data collection campaigns or large-scale annotation workflows.
- Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration.
- Experience with RL training stability techniques for large runs.
- PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.
Logistics
- 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.