Modal
Member of Technical Staff - Research, Post-Training
New York · Staff+
Sponsorship not specifiedDetected 16 days ago
Node.jsRESTLLMsValuationResearch
About the role
- 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.
Responsibilities
- 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.
- Carry and expand collaborations with outside research labs. For example, our work with ZLab on DFlash https://modal.com/blog/spec-is-all-u-need, a speculator design built on KV injection and blockwise parallel drafting.
- 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.
Requirements
- A research-leaning background in post-training LLMs, with work you can point to.
- Ability to work in-person, in our NYC or San Francisco office.
Skills
- Help shape the research agenda. None of the above is prescriptive; your work will help guide our future.
Company info
- 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 https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, 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 https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
- 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 https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, 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 https://modal.com/blog/modal-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 https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
This listing is sourced directly from Modal's careers page and normalized into a canonical job model.