Machine Learning Performance Engineer

Jane Street is hiring a Machine Learning Performance Engineer in Machine Learning — New York, New York, United States. The overview below is synthesized from the employer posting on janestreet.com: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.

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.

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.

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.

Build a standout application

For the Machine Learning Performance Engineer at Jane Street, reviewers look for concise evidence over buzzwords. Mirror the language of the posting sparingly, quantify support or delivery outcomes, and show how you handled ambiguity, time-zone collaboration and user empathy. Keep your resume to impact, keep your cover note to one page, and link to artifacts — tickets resolved, docs shipped, dashboards owned — that prove you can operate in a fast-moving Web3 team. Prepare to discuss a time you turned a confusing user report into a clear fix and how you measure quality in support and operations.

Web3 hiring values reliability: on-time follow-through, clear writing, and a track record of improving runbooks and tooling. Treat the application as a work sample. For interviews, be ready to walk through how you prioritize across time zones, handle a difficult user, and decide when to escalate versus resolve directly. Show how you document decisions so the next teammate benefits.

In a distributed Web3 org, trust builds through written clarity. Use the cover note to demonstrate it. Add links to public work, keep formatting scannable, and close with a clear ask. Hiring managers skim — make impact obvious in the first half-page.

Career growth in Web3 rewards continuous learning. Follow protocol changelogs, practice with testnets, and contribute to open issues. Small, consistent contributions compound into credibility more than one-off credentials.