The Role:We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We're looking for research depth in post-training to sit alongside our systems and product work.
You will do hands-on post-training research at Modal, working with the research lead to pick high-impact bets and owning them end to end. The work that pays off fastest is tied to production workloads -- we're already experts at training speculators for deployed models, and there are open research questions like distilling a target model from its own production traffic. There is also room to prove what the platform makes possible, where training AI scientists or kernel engineers is a natural fit given our GPU sandboxes.
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.
