Bring your own code, and run CPU, GPU, and data-intensive compute at scale. The serverless platform for AI and data teams. Backed by Accel, General Catalyst and AI Grant.
About the role
Most of the value of owning a model shows up at serving time. We're building a platform that covers the whole life of an LLM -- train it, deploy it, observe it -- and inference is where teams feel the difference every day. We already run elastic inference, sandboxes, distributed volumes, and multi-node training, and we control the infrastructure underneath, so the serving stack is ours to shape rather than something we resell.
What they're looking for
- A research-leaning or systems background in LLM inference, with work you can point to
- Fluency in the LLM serving stack, from kernels and quantization up to schedulers and autoscaling
- A record of shipping research or systems that other people build on, whether in a lab or in industry
- The drive to independently take a research bet from idea to result, working in the open with the rest of the team
- Ability to work in-person, in our NYC or San Francisco office
More about this role
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.
Most of the value of owning a model shows up at serving time. We're building a platform that covers the whole life of an LLM -- train it, deploy it, observe it...
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