Machine Learning Performance Engineer

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.

• 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 Compute-sight-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.