Member of Technical Staff - Machines

San Francisco · New York · 5+ years experience · Full-time · Onsite · Posted Sep 22, 2026

At a glance

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.

Modal is looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform.
You will be working on Modal's machines layer, which includes the fleet of bare metal and cloud hosts that run Functions, Sandboxes, and training jobs, as well as the control plane that provisions, images, monitors, and repairs them.

Automate the integration of new capacity from a growing set of hardware providers
Audit and benchmark hosts and clusters
Maintain machine images and configure GPUs, RDMA, networking, and storage
Get machines into production
Build automation that keeps the fleet healthy without human intervention by detecting bad GPUs, thermals, and disks
Dig into issues between the hardware and software that runs on top of it, such as kernel panics, broadcast storms during boot, or getting the container runtime to run on new architectures and platforms
Participate in the on-call rotation and respond to production incidents

5+ years of experience writing high-quality production code
Experience operating fleets of physical hardware (bare metal provisioning, BMC/IPMI, PXE or network boot, firmware) or building the control planes that manage them
Strong cloud skills
Strong knowledge of low-level operating system foundations (Linux kernel, drivers, networking, file systems, containers, etc.)
Effective at debugging across layers, from BGP flapping, Linux RPS, and vBIOS bugs to a Python control-plane service
Willingness to step into the thick of it with our on-call rotation and respond to production incidents