At a glance
Lead a team building Modal’s machines layer—the bare-metal fleet and control plane powering serverless functions, sandboxes, inference, and training. Stay hands-on across hardware, Linux, networking, GPUs, and distributed systems.
Summarized by AI from the original posting
What you'll do
- Lead engineers designing, building, and maintaining high-performance serverless platform systems
- Own the full lifecycle of machines from hardware acceptance and benchmarking through provisioning, monitoring, and remediation
- Manage a team of 3–8 engineers while remaining hands-on
- Guide network bring-up, kernel and image management, GPU and disk health tracking, and automated host remediation
- Set long-term technical direction and architectural decisions
- Develop automatic remediation for unhealthy machines
- Integrate new CPU, GPU, and storage servers into the fleet
- Monitor network health across datacenters
- Build hardware acceptance testing and benchmarking systems
- Manage network bootloaders, machine images, kernels, and firmware
Requirements
- 01Experience operating large fleets of physical hardware at scale or building control planes
- 02Strong cloud skills
- 03Strong knowledge of low-level operating system foundations
- 04Experience with hardware and colocation providers
- 05Track record of setting technical direction and driving architectural decisions
- 06Willingness to participate in on-call rotation and respond to production incidents
Skills
Full description
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 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,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role:
We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll own the full lifecycle of a machine, from accepting and benchmarking new hardware from a growing set of providers, to network bring-up, kernel and image management, GPU and disk health tracking, and automated remediation of unhealthy hosts. You'll manage a team of 3–8 engineers while staying hands-on across the stack which involves BMCs, firmware, PXE, bootloaders, Linux networking, drivers, and distributed control-plane services, and you'll shape our long-term path further down the stack.
Requirements:
7+ years of experience writing high-quality production code
3+ years of direct people management experience, ideally leading a team of engineers through project planning, growth, and performance conversations
Experience operating large fleets of physical hardware at scale (bare metal provisioning, BMC/IPMI, PXE and network boot, firmware) or building the control planes that manage them (the more challenges you've worked through, the better)
Strong cloud skills
Strong knowledge of low-level operating system foundations (Linux kernel, drivers, networking, file systems, containers, etc.)
Experience working with hardware and colocation providers, including hardware acceptance testing and benchmarking
Track record of setting technical direction and driving architectural decisions across a team
Willingness to step into the thick of it with our on-call rotation and respond to production incidents
Nice-to-Haves:
Experience with GPUs and the NVIDIA software stack (drivers, health monitoring, RDMA/NVLink) in production
Prior experience with Go
Key Things the Team Is Working On:
Automatic remediation of unhealthy machines (power cycling, reimaging, GPU recovery) to maximize uptime and minimize operator toil.
Automatic integration of new CPU, GPU, and storage servers into the fleet while managing hardware and network heterogeneity.
Network health monitoring and reliability across many datacenters, and standardization of bare metal network configuration.
Automatic hardware acceptance testing and benchmarking (CPU, disk, GPU, interconnect, network).
Custom network bootloader, machine image pipeline, and kernel and firmware management across the fleet.