Skip to content
CV Rocket
Get started
How it worksThe enginePricingBlogContactSign in

Open nowPosted 6 hours agoWe saw it 73 min after it went up

Member of Technical Staff - Inference Runtime — Modal Labs

General Catalyst portfolio5,597 open roles

Where
San Francisco, CA, USA
Work mode
On site
Get the CV for this job

From $25 per CV, paid once. No subscription.

Your applicationOpen nowMember of Technical Staff - Inference Runtime — Modal LabsGeneral Catalyst portfolio · San Francisco, CA, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on General Catalyst portfolio's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

8.0% of postings close within 7 days. Measured by our own scanner across the market. General Catalyst portfolio postings stay open a median of 28 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.5%3 days
  3. 8.0%7 days
  4. 15.0%14 days
  5. 34.1%30 days
This job: posted 6 hours ago

General Catalyst portfolio median: 28 days open

The posting

About the role

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.

What you’ll work on

  • 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.
  • 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.

What we’re looking for

  • 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.

Particularly relevant experience

Any of the following would be helpful, but none is required:

  • gVisor, runsc, runc, OCI runtimes, seccomp, or checkpoint/restore systems.
  • Linux kernel development, virtualization, sandboxing, kernel modules, or device proxying.
  • CUDA, ROCm, GPU drivers, accelerator virtualization, or GPU profiling.
  • RDMA and high-performance networking.
  • FUSE, EROFS, direct I/O, mmap, page-cache behavior, or storage engines.
  • Large-memory or multi-GPU inference and training systems.
  • Contributions to open-source systems software.

Prior gVisor or machine-learning experience is not required. We care more about strong systems fundamentals, curiosity, and the ability to learn unfamiliar parts of the stack.

Why this role

The container runtime is directly on the critical path for inference performance. Improvements here can substantially reduce cold starts, increase token throughput, unlock new accelerator types, and make previously impractical workloads possible.This is an opportunity to work on unusually deep systems problems with immediate production impact. You’ll have room to shape the architecture, contribute to open-source runtime technology, and take ownership of foundational infrastructure used by every inference and training workload on Modal.

From $25, paid onceGet the CV for this job

What happens when you press

One press. We do the rest.

  1. A CV for this posting

    Written against General Catalyst portfolio's own wording, from every piece of relevant proof in your profile.

  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on General Catalyst portfolio's form

    Our application engine gets through the hardest forms there are. Where a question needs you, AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    General Catalyst portfolio's answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

    AI helps you write the email, checks it and sends it. We show you whether the recruiter read it.

  6. The interview in your calendar

    Full integration with your calendar. The invitation goes straight in.

    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
Get the CV for this job

From $25 per CV, paid once. No subscription.

Why it works

3×

more interviews than doing it yourself with ChatGPT.

ChatGPT writes a CV and never learns what happened to it. We see every reply. For each CV we know:

  • How it was written, and how the review scored it
  • When we applied, and how long after the posting went up
  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

Get the CV for this job

From $25 per CV, paid once. No subscription.

The numbers game

More applications. More interviews.

Every application goes out with its own CV, written for that posting and paid once. Send enough of them and the law of large numbers finds you the job.

Get the CV for this job
By hand5–10
With CV Rocket100
applications a day

Nearby

Live postings like this one

Same employer first, then the same role elsewhere.

Before you press

Straight answers

Get the CV for this job

From $25 per CV, paid once. No subscription.

What if my background isn't good enough?

We make the most of the background you have. The CV uses every piece of relevant proof your profile holds, and one of the 25 readers reads your whole profile and flags what the CV left out.

Do you really apply for me?

Yes, on the employer's own form, the hardest ones included. Where a question needs you, you answer it right there and AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

Is it a subscription?

No. You pay once per CV, from $25. Every application goes out with its own CV, written for that posting.

One job. One CV.
Paid once.

Pick the posting you want. We write for it, apply for you and catch the reply.

Get the CV for this job

From $25 per CV, paid once. No subscription.