Staff Systems Engineer, High-Performance Container Runtime

Creandum; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

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.

Qualifications

• Strong Linux systems knowledge including processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals.
• Experience building or debugging container runtimes, sandboxes, Linux kernel, or similar low-level infrastructure.
• Strong programming ability in Rust or Go and willingness to work in both.
• Experience profiling and improving systems where memory movement, I/O, synchronization, or kernel interactions dominate performance.

Responsibilities

• Make container startup, checkpoint, and restore dramatically faster for large inference and training workloads.
• Build multi-GPU and accelerator-aware snapshotting for GPU memory and RDMA workloads.
• Design zero-copy and low-copy data paths between container memory, filesystems, storage, and runtime.
• Optimize large snapshot pipelines using parallel uploads, direct I/O, incremental snapshots, and efficient memory handling.
• Improve container image and filesystem performance across EROFS, FUSE, page caches, overlays, and remote storage.
• Extend 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.

Skills

Linux systems knowledge

Container runtimes

Rust

Go

Performance profiling

Kernel debugging

Systems design

Ownership mindset

Tools

gVisor

runsc

OCI runtimes

Job description

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.