RL Research Scientist — Language Models

We are running this search on behalf of a client building frontier models, and they need researchers who can push reinforcement learning forward at the level of language models, not just apply it. This is hands-on research with a tight loop between ideas, experiments, and what ships into real systems.

What you will work on

• Reinforcement learning methods for large language models: post-training, reward modeling, and the algorithms that make models more capable and more aligned with what we actually want from them.
• Designing and running experiments at scale, then turning what you learn into methods the rest of the team can build on.
• Open problems rather than closed tickets. You will help decide what is worth working on, not just execute someone else's spec.

Who fits

• Strong foundations in reinforcement learning, including the core ideas that predate the current wave. You understand why methods work, not only how to call them.
• Direct experience applying RL to language models. This could be RLHF, RLAIF, preference optimization, reward modeling, or related post-training work.
• A track record of research you can point to: published papers, strong open-source contributions, or shipped methods with measurable impact.
• Comfort owning ambiguous problems end to end, from framing the question to landing the result.

Nice to have

• Experience training or post-training models at scale.
• A history of moving quickly from idea to working experiment.
• Strong written communication. We share results across the team often.