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Member of Technical Staff - Inference Runtime

Modal· Engineering
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San Francisco$220k - $300kFull-timePosted 2w ago

About the role

Frontend-free, low-level systems role building and optimizing Modal’s container runtime for inference and training workloads, with emphasis on Linux, containers, GPUs, and performance-focused runtime infrastructure developed in Rust and Go.

Responsibilities

  • 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 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.

Qualifications

  • Strong Linux systems knowledge, particularly processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals.
  • Experience building or debugging container runtimes, sandboxes, Linux kernel, or similarly low-level infrastructure.
  • Strong programming ability in Rust, Go, or another systems language, with an 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.
  • Comfort debugging across abstraction boundaries, from application behavior down through runtimes, drivers, and the kernel.
  • An ability to turn loosely defined production problems into well-designed, reliable systems.
  • A desire to own important infrastructure and work closely with both internal teams and customers.

Skills

Required:Linux systemsContainer runtimesRustGoPerformance profilingDebugging across boundaries
Nice to have:Open-source collaboration
Last updated Oct 21, 2026. Interested? Apply on the company site.
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About Modal

High-performance AI infrastructure

Bring your own code, and run CPU, GPU, and data-intensive compute at scale. The serverless platform for AI and data teams.

AI infrastructureBatchGPU scalingInferenceML workloadNotebooksObservabilityPlatform
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