Member of Technical Staff
Conduct hands-on post-training research on LLMs including RL, distillation, and routing models. Collaborate with customers, labs, and engineering to turn techniques into production products and shape the research agenda. Requires proven research background in post-training LLMs and ability to ship impactful work.
About the job
What you'll do
- Own end-to-end post-training research bets: async and agentic RL, on-policy distillation, long-context RL, small routing models, and whatever else the research agenda calls for.
- Work directly with customers alongside our Forward Deployed Engineers to train models and bring what you learn back into the research.
- Carry and expand collaborations with outside research labs. For example, our work with ZLab on DFlash, a speculator design built on KV injection and blockwise parallel drafting.
- Work with engineering to turn frontier post-training techniques into products: an opinionated post-training framework, distributed-training approaches (DiLoCo, evolutionary strategies), online training for deployed models, and more.
- Help shape the research agenda. None of the above is prescriptive; your work will help guide our future.
Requirements
- A research-leaning background in post-training LLMs, with work you can point to.
- Enough product sense to tell which frontier techniques matter to users and which stay academic.
- A record of shipping research that other people build on, whether in a lab or in industry.
- The drive to take a research bet from idea to result without much hand-holding, working in the open with the rest of the team.
- Ability to work in-person, in our NYC or San Francisco office.
Skills
Post-Training Llms, Reinforcement Learning, Distillation, Long-Context Rl, Routing Models, Diloco, Evolutionary Strategies, Kv Injection, Blockwise Parallel Drafting
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