# Machine Learning Performance Engineer at Jane Street

> United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Ajanestreet%3A7449077002 (read 2026-11-06).

## Facts (observed)

- Company: [Jane Street](https://alderhq.app/companies/jane-street)
- Location text: New York, New York, United States; New York
- Where it can be done from (inferred): United States (evidence: "Location: New York, New York, United States; New York")
- Posted: 2024-05-20 (900 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://www.janestreet.com/join-jane-street/apply/7449077002?gh_jid=7449077002
- Skills listed (inferred): CSS

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (New York, n 90): p25 $177k, median $233k, p75 $275k

## Description (the employer's text)

We are looking for an engineer with experience in low-level systems programming and optimization to join our growing ML team.

Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction.

Your part here is optimizing the performance of our models – both training and inference. We care about efficient large-scale training, low-latency inference in real-time systems, and high-throughput inference in research. Part of this is improving straightforward CUDA, but the interesting part needs a whole-systems approach, including storage systems, networking, and host- and GPU-level considerations. Zooming in, we also want to ensure our platform makes sense even at the lowest level – is all that throughput actually goodput? Does loading that vector from the L2 cache really take that long?

If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in.

There’s no fixed set of skills, but here are some of the things we’re looking for:

- An understanding of modern ML techniques and toolsets
- The experience and systems knowledge required to debug a training run’s performance end to end
- Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores, and the memory hierarchy
- Debugging and optimization experience using tools like CUDA GDB, NSight Systems, NSight Compute
- Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN, and cuBLAS
- Intuition about the latency and throughput characteristics of CUDA graph launch, tensor core arithmetic, warp-level synchronization, and asynchronous memory loads
- Background in Infiniband, RoCE, GPUDirect, PXN, rail optimization, and NVLink, and how to use these networking technologies to link up GPU clusters
- An understanding of the collective algorithms supporting distributed GPU training in NCCL or MPI
- An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools
If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Ajanestreet%3A7449077002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.