Thinking Machines Lab
Research, Post-Training
San Francisco
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About the role
- The role of post-training researchers sits at the core of our roadmap.
- This is the critical bridge between raw model intelligence and a system that is actually useful, safe, and collaborative for humans.
- This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally.
Responsibilities
- Develop and tune the recipe: iterate on post-training recipes, consisting of a collection of datasets, training stages, and hyperparameters. Measure how recipe choices affect various metrics.
- You'll be responsible for both getting things to work, and developing a deeper understanding, which we can bring to the next problem.
- Scale and explore: post-training will involve a combination of scaling the existing methodologies and developing new ones.
- Develop and tune the recipe: iterate on post-training recipes, consisting of a collection of datasets, training stages, and hyperparameters.
- We believe the future worth building is human, and we're hiring people who want to build it.
Requirements
- Clarity in communication, an ability to explain complex technical concepts in writing.
- You can look at experimental data and distinguish between real effects, noise, and bugs.
- Experience managing or analyzing human data collection campaigns or large-scale annotation workflows.
Nice to have
- we encourage you to apply even if you don't meet all preferred qualifications, but at least some:
Compensation
- Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Benefits
- Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
- Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).
- Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
- Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models.
Company info
- 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.
Visa & Work Authorization
- 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.
This listing is sourced directly from Thinking Machines Lab's careers page and normalized into a canonical job model.