Machine Learning Performance Optimization Specialist

4 weeks ago

Hong Kong, Central and Western District, Hong Kong SAR China Jane Street Full time

We are seeking a skilled engineer with expertise in low-level systems programming and optimization to contribute to our growing ML team at Jane Street.

As a critical component of our global business, machine learning serves as a unique platform for experimentation and innovation. Our trading environment provides a rapid-feedback loop, allowing us to incorporate new ideas with minimal friction.

Your role will focus on optimizing the performance of our models, encompassing both training and inference. This involves efficient large-scale training, low-latency inference in real-time systems, and high-throughput inference in research. You will work on improving CUDA performance, as well as adopting a whole-systems approach, including storage systems, networking, and host- and GPU-level considerations.

To ensure our platform is optimized at all levels, you will investigate whether throughput is actually goodput and whether loading vectors from the L2 cache takes a significant amount of time.

If you're new to the finance industry, you're in good company. Many of our team members were in the same position before joining Jane Street. If you have a curious mind and a passion for solving complex problems, we believe you'll fit right in.

We're looking for candidates with the following skills:

  • Knowledge of modern ML techniques and toolsets
  • 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 optimization experience using tools like CUDA GDB, NSight Systems, NSight Compute, 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 optimization, and NVLink, and how to use these networking technologies to link up GPU clusters
  • Understanding of collective algorithms supporting distributed GPU training in NCCL or MPI
  • Inventive approach and willingness to ask hard questions about whether we're taking the right approaches and using the right tools
  • Fluency in English

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