Machine Learning Performance Engineer

2 Months ago • All levels • Research Development

Job Summary

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.
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.
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.

Job Details

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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About The Company

Jane Street is a quantitative trading firm with offices in New York, London, Hong Kong, Singapore, and Amsterdam. We are always recruiting top candidates and we invest heavily in teaching and training. The environment at Jane Street is open, informal, intellectual, and fun. People grow into long careers here because there are always new and interesting problems to solve, systems to build, and theories to test.



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