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About the Role

This research role focuses on scaling reinforcement learning for frontier models, with a core interest in asynchronous RL and co-designing the RL recipe with its supporting systems. The position requires a deep understanding of both ML and systems aspects, particularly concerning inference efficiency and large-scale training.

Responsibilities

• Co-design the RL recipe and the systems that run it, making joint recipe-level and systems-level choices and validating them at frontier scale.
• Advance asynchronous RL algorithms.
• Improve the efficiency of rollout generation and its integration with training, treating inference as a first-class part of the RL loop.
• Run frontier-scale RL end to end, including bringing up new models and training setups, and maintaining stability of large runs.
• Jointly optimize the compute and training efficiency of RL, focusing on accelerator utilization, memory, communication, and low-precision numerics.
• Conduct empirical science, including ablations and scaling studies, supported by reliable instrumentation and clear documentation.

Requirements

• 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 and the ability to explain complex technical concepts in writing.
• Strong research judgment, demonstrated through clean ablations, honest baselines, and clear technical writing.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.
• Strong grounding in RL for large language models, such as modern policy optimization methods and their behavior at scale.
• Deep understanding of asynchronous RL: the algorithms, design choices, and trade-offs, on both the ML and the systems sides.
• Experience training large models across many accelerators, with comfort inside the distributed stack (parallelism strategies, memory, communication).
• Good working knowledge of inference systems: able to reason quantitatively about rollout generation throughput and cost.
• Experience building or operating decoupled generation/training RL systems at scale.
• Experience with RL on verifiable and agentic tasks, including multi-turn environments.
• 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.

Skills

• Python
• Deep learning frameworks (PyTorch, TensorFlow, JAX)
• Distributed training
• Reinforcement Learning (RL)
• Asynchronous RL
• Inference systems
• Low-precision numerics
• Quantization
• LLM serving stacks (SGLang, vLLM, TokenSpeed, or custom engines)

Location

• San Francisco, California

Work Type

• Full-time

Experience Level

• Research

Education Level

• Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline or equivalent industry research experience

Salary/Compensations

• $350,000 - $475,000 USD

Benefits

• Generous health, dental, and vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support

About the Company

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

Equal Opportunity

• 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.