Research, Post-Training

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health benefits

Dental benefits

Vision benefits

Unlimited PTO

Parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. in San Francisco, CA, seeks a post-training researcher to bridge raw model intelligence with practical, safe, and collaborative AI. You will write high-performance code and digest technical reports, blending theory with hands-on experimentation to shape how AI learns.

You will develop and tune post-training recipes, evaluate metrics, debug configurations, scale methodologies, and publish research that advances the field for industry and academia.

Qualifications

• 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.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Responsibilities

• Develop and tune the recipe: iterate on post-training recipes, consisting of datasets, training stages, and hyperparameters; measure how recipe choices affect metrics.
• Iterate on evals: define evaluations, optimize them, and ensure numbers reflect meaningful progress.
• Debug and understand: investigate results that don’t make sense and improve training configurations.
• Scale and explore: scale existing methods and develop new ones; compare performance vs dataset size.
• Publish and present research that moves the community forward. Share code, datasets, and insights.

Skills

Python

PyTorch

TensorFlow

JAX

Education

Bachelor's degree or equivalent in CS/ML/Physics/Math

Job description

The mission of Thinking Machines is to build AI that extends human will and judgment.

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. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands‑on experimentation, and who wants to shape the foundations of how AI learns.

What You’ll Do

• 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.

• Iterate on evals: post-training involves a never-ending loop of defining a set of evaluations, optimizing them, and then realizing your existing evals don’t capture what matters. You’ll be responsible for both making numbers go up, and making sure the numbers are meaningful.

• Debug and understand: while tuning the details of a training configuration, we often observe results that don’t quite make sense. 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. We’ll want to both measure how performance metrics scale with dataset size, and explore using a completely different kind of training dataset.

• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

Skills and Qualifications

Minimum qualifications:

• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.

• 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.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• 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.

• 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.