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.

Our trading environment serves as a unique platform for ML experimentation, allowing us to incorporate new ideas with minimal friction.

Your role will involve optimizing the performance of our models, focusing on efficient large-scale training, low-latency inference in real-time systems, and high-throughput inference in research.

Key aspects of this position include improving CUDA performance, optimizing storage systems, networking, and host- and GPU-level considerations.

We aim to ensure our platform is efficient even at the lowest level, considering throughput and goodput.

While prior experience in finance is not required, a curious mind and passion for solving complex problems are essential.

Some of the skills we're looking for include:

  • Understanding of modern ML techniques and toolsets
  • Experience in debugging training run performance end to end
  • Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores, and 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 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 question current approaches and tools
  • Fluency in English


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