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# Machine Learning Performance Engineer

- **Company:** Jane Street
- **Location:** UK
- **Contract:** Permanent contract
- **Skills:** Computer Clusters, Nvidia CUDA, Software Debugging, InfiniBand, Machine Learning, System Programming, Computer Network Technologies, Real Time Systems, Syntactically Awesome Style Sheets (SASS)
- **Published:** August 1, 2026
- **Apply:** https://www.totaljobs.com/job/machine-learning-engineer/jane-street-europe-limited-job107778709

## About the Role

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

## Description

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?


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