# Machine Learning Performance Engineer - Jane Street [Janestreet](https://gurify.com/jobs?q=Janestreet) · New York, United States · Posted 29 months ago [AI & ML](https://gurify.com/jobs/ai-ml) [Apply on the original posting → (opens in a new tab)](https://job-boards.greenhouse.io/janestreet/jobs/7449077002) ## Job description 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. **Live in Janestreet’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role. 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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.\u003C/p\u003E\u003Cp\u003EYour part here is optimizing the performance of our models \u2013 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 \u2013 is all that throughput actually goodput? Does loading that vector from the L2 cache really take that long?\u003C/p\u003E\u003Cp\u003EIf you\u2019ve never thought about a career in finance, you\u2019re in good company. Many of us were in the same position before working here. 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