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Machine Learning Performance Engineer Job Opening In Hong Kong, Hong Kong – Now Hiring Jane Street


Job description

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


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 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 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

  • Fluency in English



If you're a recruiting agency and want to partner with us, please reach out to .

Required Skill Profession

Computer Occupations


  • Job Details

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Unlock Your Machine Learning Potential: Insight & Career Growth Guide


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Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph here. Uncover the dynamic job market trends for Machine Learning in Hong Kong, Hong Kong, highlighting market share and opportunities for professionals in Machine Learning roles.

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Are You Looking for Machine Learning Performance Engineer Job?

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The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at Jane Street adheres to the cultural norms as outlined by Expertini.

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