The opportunity
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.
What you'll do
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
What they're looking for
- 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
