Machine Learning Performance Engineer

4 Months ago • All levels
Research Development

Job Description

The job involves optimizing the performance of machine learning models, both training and inference, focusing on efficient large-scale training, low-latency inference, and high-throughput inference. This includes improving CUDA and taking a whole-systems approach, considering storage systems, networking, and host- and GPU-level considerations. The role requires debugging training run performance end-to-end and understanding the intricacies of GPU hardware and networking technologies.
Good To Have:
  • Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN and cuBLAS
  • Intuition about the latency and throughput characteristics
  • Background in Infiniband, RoCE, GPUDirect, PXN, rail optimization.
  • An understanding of the collective algorithms supporting distributed GPU training.
Must Have:
  • Understanding of modern ML techniques and toolsets.
  • Experience to debug a training run's performance end to end.
  • Low-level GPU knowledge of PTX, SASS, warps, etc.
  • Debugging and optimization experience using tools like CUDA GDB.

Add these skills to join the top 1% applicants for this job

problem-solving
data-structures
cuda
networking
sass
algorithms
machine-learning

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 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 agency-partnerships@janestreet.com.

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