or 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

Location & work details

Office location and remote eligibility are separate. A country flag describes the stated location, not who may apply.

Original location: San Francisco
Country: San Francisco (ATS location fields)
Work mode: OnSite (ATS workplaceType/location)
Salary: $220K - $300K (ATS compensation field)
Stack: 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. (Source language/tool mention)
Stack: 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. (Source language/tool mention)
Stack: oads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, a (GTM description)
Stack: ontainers, filesystems, storage, GPU drivers, and distributed systems.Our goal is to make demanding ML workloads start (GTM description)
Normalization: Explicit source location text; no city-only country inference.

Employer posting

Member of Technical Staff - Inference Runtime

Source details

Company identifier: modal
Source ID: ashby:modal:6195f552-ca6f-4540-b7f6-e7e6054e47ad
Board: ashby:modal
Evidence field: description
Match rule: alternative_in_list
Go matches in source: 5
Department as supplied: Engineering

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