Thinking Machines Lab
Research, RL Scaling
San Francisco
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About the role
- Our team scales reinforcement learning for frontier models.
- Progress in RL is increasingly set by how well it scales: more rollouts, larger models, and training loops that keep large fleets of accelerators doing useful work.
- We are particularly interested in people working on high-training-compute, long-horizon RL.
Responsibilities
- Co-design the RL recipe and the systems that run it: make recipe-level choices jointly with systems-level ones and validate them at frontier scale.
- Jointly optimize the compute and training efficiency of RL: accelerator utilization, memory, communication, and low-precision numerics.
Requirements
- Clarity in communication, an ability to explain complex technical concepts in writing.
- Experience training large models across many accelerators, with comfort inside the distributed stack (parallelism strategies, memory, communication).
- Experience with RL on verifiable and agentic tasks, including multi-turn environments.
- Experience with RL training stability techniques for large runs.
- Experience with scaling studies for large models.
- Good working knowledge of inference systems: able to reason quantitatively about rollout generation throughput and cost.
- Familiarity with low-precision training and inference: numerics, quantization, and their implications for RL.
Nice to have
- we encourage you to apply if you meet some but not all of these:
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.
- PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding
Company info
- Improve the efficiency of rollout generation and its integration with training, treating inference as a first-class part of the RL loop.
- The mission of Thinking Machines is to build AI that extends human will and judgment.
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.