Research Lead, Tinker, Fine-tuning Science

Thinking Machines Lab•San Francisco, CA

1d•$475,000 - $530,000•Onsite

About The Position

Thinking Machines is building AI that extends human will and judgment. They are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. The company believes the future worth building is human and is hiring people who want to build it. In this role, you will 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 where you will stay hands-on in the science while growing the team, shaping its direction, and ensuring findings ship into Tinker. You will work closely with internal research teams, contribute to open science, and engage with external users.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.