Research, Finetuning Science

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.

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.

In this role, you'll work on frontier customization techniques and help build the best post-training engine in the industry – Tinker – drawing on a whole-stack understanding of RL science. Findings directly shape Tinker's training defaults, API design, and the open-source Tinker Cookbook. You'll work with our internal research teams as well as contributing to open science for external partners.

In this role, you'll advance the science of fine-tuning and frontier post-training techniques. You’ll:

• Contribute to areas like LoRA and parameter efficient fine-tuning and how to push customization quality, efficiency, and reliability to the frontier.

• Ship research into product: inform Tinker's training defaults and primitives, and codify best-practice methods as recipes in the Tinker Cookbook.

• Improve the stability, efficiency, and reliability of large-scale fine-tuning and RL runs on Tinker.

• Share what you learn through papers, technical blog posts, and community contributions.

You’ll contribute to areas like LoRA, parameter-efficient fine-tuning, how things interact with RL and post-training, and how to push customization quality, efficiency, and reliability to the frontier.

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.

• 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.

• 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.

• 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.