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

At Modal, we sell cloud services atop which our customers run their critical production systems. As a rapidly growing new cloud infrastructure company, we seek to improve our reliability dramatically while scaling the size of our platform, customer base, and team.

This role is for people who are deep systems thinkers, love stacking nines, and thrive on making others move faster at scale.

What You'll Work On

Identify architectural changes to improve reliability and performance

Foster a culture of reliability across Modal’s engineering organization

Define and implement operational processes such as deployments and upgrades

Operate infrastructure like Kubernetes, Postgres, and Redis

Participate in on-call rotations and respond to production incidents

You May Be a Fit If

You have 5+ years of experience writing high-quality production code

You have 2+ years of on-call experience for critical production services

You have strong cloud skills and deep familiarity with at least one hyperscaler cloud (AWS preferred)

You’re familiar with autoscaling, fleet management, and capacity planning at scale

You’ve operated databases, monitoring, CI/CD, and other infrastructure at scale

You’re able to work in person in our NYC or Stockholm offices

Nice-to-Have

Experience owning and scaling Kubernetes clusters to thousands of nodes

Experience with systems safety research (e.g., STAMP) and control theory