{"active":true,"apply_url":"https://www.janestreet.com/join-jane-street/apply/7449190002?gh_jid=7449190002","company":{"claimed":false,"name":"Jane Street","slug":"jane-street","website":"https://www.janestreet.com"},"countries":["HK"],"employment_type":null,"first_seen_at":"2026-10-31T10:15:09Z","getwork_url":"https://getwork.dev/jobs/gh-jane-street-7449190002/","id":"gh-jane-street-7449190002","injection_flag":false,"last_seen_at":"2026-11-03T12:00:09Z","location":"Hong Kong, Hong Kong","published_at":"2024-05-20T21:22:15Z","regions":["apac"],"remote":"onsite","role_family":"ml","salary":null,"seniority":"unspecified","stack":[],"stack_mentioned":[],"title":"Machine Learning Performance Engineer","untrusted_description":"We are looking for an engineer with experience in low-level systems programming and optimisation to join our growing ML team.\n\nMachine 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.\n\nYour 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?\n\nIf 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.\n\nThere’s no fixed set of skills, but here are some of the things we’re looking for:\n\n- An understanding of modern ML techniques and toolsets\n\n- The experience and systems knowledge required to debug a training run’s performance end to end\n\n- Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores and the memory hierarchy\n\n- Debugging and optimisation experience using tools like CUDA GDB, NSight Systems, NSight Computesight-systems and nsight-compute\n\n- Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN and cuBLAS\n\n- Intuition about the latency and throughput characteristics of CUDA graph launch, tensor core arithmetic, warp-level synchronization and asynchronous memory loads\n\n- Background in Infiniband, RoCE, GPUDirect, PXN, rail optimisation and NVLink, and how to use these networking technologies to link up GPU clusters\n\n- An understanding of the collective algorithms supporting distributed GPU training in NCCL or MPI\n\n- An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools\n\n- Fluent in English\n\nIf you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.","url":"https://www.janestreet.com/join-jane-street/apply/7449190002?gh_jid=7449190002"}