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.

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 .