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Member of Technical Staff

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Member of Technical Staff in USA

  • San Francisco, CA

Responsibilities

  • Improve container startup, checkpoint, and restore performance for large inference and training workloads.
  • Build multi-GPU and accelerator-aware snapshotting for GPU memory and RDMA-enabled workloads.
  • Design zero-copy and low-copy data paths across container memory, filesystems, storage, and Modal’s runtime.
  • Optimize snapshot pipelines using parallel uploads, direct I/O, incremental snapshots, and improved memory handling.
  • Improve container image and filesystem performance across EROFS, FUSE, page caches, overlay filesystems, and remote storage.
  • Extend the sandboxed runtime for new GPUs, drivers, profiling tools, and device capabilities across NVIDIA and AMD hardware.
  • Debug failures involving system calls, virtual memory, process lifecycle, kernel behavior, GPU drivers, and container isolation.
  • Roll out runtime and kernel changes using compatibility controls, scheduling constraints, feature flags, and observability.
  • Work across runtime, scheduler, storage, and GPU infrastructure from investigation through production deployment.
  • Contribute to gVisor and upstream runtime technology when appropriate.

Requirements

  • Strong Linux systems knowledge covering processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals.
  • Experience building or debugging container runtimes, sandboxes, the Linux kernel, or similarly low-level infrastructure.
  • Strong programming ability in Rust, Go, or another systems language, with interest in becoming productive in both Rust and Go.
  • Experience profiling and improving systems where memory movement, I/O, synchronization, or kernel interactions dominate performance.
  • Ability to debug across application, runtime, driver, and kernel abstraction boundaries.
  • Ability to turn loosely defined production problems into reliable systems.
  • Helpful but not required experience includes gVisor, runsc, runc, OCI runtimes, seccomp, checkpoint/restore systems, Linux kernel development, virtualization, sandboxing, kernel modules, device proxying, CUDA, ROCm, GPU drivers, accelerator virtualization, GPU profiling, RDMA, high-performance networking, FUSE, EROFS, direct I/O, mmap, page-cache behavior, storage engines, large-memory or multi-GPU inference and training systems, and open-source systems software.

Benefits

  • Opportunity to shape the architecture of foundational container runtime infrastructure used by Modal’s inference and training workloads.
  • Opportunity to contribute to open-source runtime technology and solve deep systems problems with direct production impact.

Salary: $220k - $300k/yr

Modal

Customers 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. Every era of computing came with new workloads that previous infrastructure couldn't se...

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