We are looking for an engineer with experience in low-level systems programming and optimisation 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 optimising 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 youve never thought about a career in finance, youre 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 youll fit right in. Theres no fixed set of skills, but here are some of the things were looking for: An understanding of modern ML techniques and toolsets The experience and systems knowledge required to debug a training runs performance end to end Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores and the memory hierarchy Debugging and optimisation experience using tools like CUDA GDB, NSight Systems, NSight Computesight-systems and 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 optimisation 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 Accepted file types: pdf, doc, docx, txt, rtf Enter manually Accepted file types: pdf, doc, docx, txt, rtf Additional information (for source) Have you interviewed with Jane Street before? * Select... Pronouns Are you currently a student? * Select... Enter your college/university Major/Field of study Extra Why youre interested in Jane Street

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