Job 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 Ramp modal.com, 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 github.com github.com), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal’s Inference Runtime team owns the container runtime stack used to run inference and training workloads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, and distributed systems. Our goal is to make demanding ML workloads start quickly, run efficiently, and remain securely isolated, whether they use a single GPU, hundreds of gigabytes of memory, or multiple GPUs connected with RDMA. We’re looking for a systems engineer who enjoys working deep in the stack. You’ll build production runtime infrastructure in Rust and Go, diagnose difficult Linux and performance problems, and help determine the architecture of Modal’s container platform. You’ll also work closely with maintainers of gVisor and contribute to the runtime itself when the right fix belongs upstream. WHAT YOU'LL WORK ON: - Make container startup, checkpoint, and restore dramatically faster for large inference and training workloads - Build multi-GPU and accelerator-aware snapshotting, including efficient handling of GPU memory and RDMA-enabled workloads - Design zero-copy and low-copy data paths between container memory, filesystems, storage, and Modal’s runtime - Optimize large snapshot pipelines using techniques such as parallel uploads, direct I/O, incremental snapshots, and more efficient memory handling - Improve container image and filesystem performance across EROFS, FUSE, page caches, overlay filesystems, and remote storage - Extend our sandboxed runtime to support new GPUs, drivers, profiling tools, and device capabilities across NVIDIA and AMD hardware - Debug complex failures involving system calls, virtual memory, process lifecycle, kernel behavior, GPU drivers, and container isolation - Safely roll out runtime and kernel changes across a heterogeneous fleet using compatibility controls, scheduling constraints, feature flags, and observability - Work across the runtime, scheduler, storage, and GPU infrastructure, and take ambiguous production problems from investigation through deployment YOU MAY BE A FIT IF: - You have strong Linux systems knowledge, particularly processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals - You’ve built or debugged container runtimes, sandboxes, the Linux kernel, or similarly low-level infrastructure - You have strong programming ability in Rust, Go, or another systems language, and an interest in becoming productive in both Rust and Go - You’ve profiled and improved systems where memory movement, I/O, synchronization, or kernel interactions dominate performance - You’re comfortable debugging across abstraction boundaries, from application behavior down through runtimes, drivers, and the kernel - You can turn loosely defined production problems into well-designed, reliable systems - You want to own important infrastructure and work closely with both internal teams and customers NICE-TO-HAVE: Any of the following would be helpful, but none is required: - gVisor, runsc, runc, OCI runtimes, seccomp, or checkpoint/restore systems - Linux kernel development, virtualization, sandboxing, kernel modules, or device proxying - CUDA, ROCm, GPU drivers, accelerator virtualization, or GPU profiling - RDMA and high-performance networking - FUSE, EROFS, direct I/O, mmap, page-cache behavior, or storage engines - Large-memory or multi-GPU inference and training systems - Contributions to open-source systems software Prior gVisor or machine-learning experience is not required. We care more about strong systems fundamentals, curiosity, and the ability to learn unfamiliar parts of the stack. WHY THIS ROLE: The container runtime is directly on the critical path for inference performance. Improvements here can substantially reduce cold starts, increase token throughput, unlock new accelerator types, and make previously impractical workloads possible. This is an opportunity to work on unusually deep systems problems with immediate production impact. You’ll have room to shape the architecture, contribute to open-source runtime technology, and take ownership of foundational infrastructure used by every inference and training workload on Modal.

Requirements

Must-haves

Our goal is to make demanding ML workloads start quickly, run efficiently, and remain securely isolated, whether they use a single GPU, hundreds of gigabytes of memory, or multiple GPUs connected with RDMA.
Make container startup, checkpoint, and restore dramatically faster for large inference and training workloads
Build multi-GPU and accelerator-aware snapshotting, including efficient handling of GPU memory and RDMA-enabled workloads
Design zero-copy and low-copy data paths between container memory, filesystems, storage, and Modal’s runtime
Optimize large snapshot pipelines using techniques such as parallel uploads, direct I/O, incremental snapshots, and more efficient memory handling
Improve container image and filesystem performance across EROFS, FUSE, page caches, overlay filesystems, and remote storage
Extend our sandboxed runtime to support new GPUs, drivers, profiling tools, and device capabilities across NVIDIA and AMD hardware
Debug complex failures involving system calls, virtual memory, process lifecycle, kernel behavior, GPU drivers, and container isolation
Safely roll out runtime and kernel changes across a heterogeneous fleet using compatibility controls, scheduling constraints, feature flags, and observability
Work across the runtime, scheduler, storage, and GPU infrastructure, and take ambiguous production problems from investigation through deployment
You have strong Linux systems knowledge, particularly processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals
You’ve built or debugged container runtimes, sandboxes, the Linux kernel, or similarly low-level infrastructure
You have strong programming ability in Rust, Go, or another systems language, and an interest in becoming productive in both Rust and Go
You’ve profiled and improved systems where memory movement, I/O, synchronization, or kernel interactions dominate performance
You’re comfortable debugging across abstraction boundaries, from application behavior down through runtimes, drivers, and the kernel
You can turn loosely defined production problems into well-designed, reliable systems
You want to own important infrastructure and work closely with both internal teams and customers

Nice-to-haves

gVisor, runsc, runc, OCI runtimes, seccomp, or checkpoint/restore systems
Linux kernel development, virtualization, sandboxing, kernel modules, or device proxying
CUDA, ROCm, GPU drivers, accelerator virtualization, or GPU profiling
RDMA and high-performance networking
FUSE, EROFS, direct I/O, mmap, page-cache behavior, or storage engines
Large-memory or multi-GPU inference and training systems
Contributions to open-source systems software

Application prep

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Keywords to mirror

aimlrustgo

Platform: ashby
Effort: quick