About the RoleTinker is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs to open access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training models with their own data, algorithms, and for their own needs.
This role is all about building our training systems for Tinker, including RL systems, numerics, kernels, and beyond.
What You'll DoIn this role, you'll develop frontier customization techniques and help build the best post-training engine in the industry, drawing on a whole-stack understanding recipes, data pipelines, and training systems (numerics, kernels, and beyond).
You'll engage directly with the researchers and companies pushing Tinker to its limits. This role is working with both our internal research teams as well as contributing to open science and external partners.
You'll co-design RL algorithms and training systems across the whole stack, from RL science down to numerics and kernels, to enable anyone to post-train frontier models. You'll debug RL runs in the wild, optimize post-training pipelines, and help users reach frontier-level results, which in turn makes our platform and models the best they can be.
Skills and QualificationsRequired 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 working on Tinker and increasing usefulness and adoption.
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.
- Experience with RL training stability techniques for large runs.
- Familiarity with low-precision training and inference: numerics, quantization, and their implications for RL.
- Hands-on work with LLM serving stacks (e.g., SGLang, vLLM, TokenSpeed, or custom engines).
- Experience with scaling studies for large models.
- Contributions to open-source training or inference frameworks.
- 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.
