About the role
About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
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.