About this 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 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've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role:
We’re looking for strong backend engineers who love building developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day.
You May Be a Fit If:
You’ve built and shipped modern web applications end-to-end
You’re comfortable working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics
You have deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes and training and inference for large language models (LLMs) and diffusion models
You have experience with at least one of the following: billing/payments systems, B2B SaaS tooling, enterprise software, or LLM/diffusion model inference and training workloads
You have strong product instincts and think about customer problems, not just tickets
You’re able to participate in our on-call rotation and respond to production incidents
You make good tradeoffs between shipping fast and building for scale
You’re able to work in person in our NYC or SF office