Machine Learning Performance Engineer

Janestreet; Greater London; On-site; GBP 120,000 - 160,000; Full time

Job summary

Jane Street in London is seeking an engineer with a focus on low-level systems programming and optimisation for our ML team. You will optimize performance of models during training and inference across large-scale systems.

The role emphasizes real-time, low-latency inference, high-throughput research workloads, and a whole-systems approach from GPUs to storage and networking. Fluency in English and curiosity about finance are valued traits.

Qualifications

• Understanding of modern ML techniques and toolsets.
• Ability to debug a training run's performance end to end.
• Experience with low-level GPU memory hierarchy and PTX/SASS concepts.
• Experience debugging with CUDA GDB and Nsight tools.
• Familiarity with ML libraries such as Triton, CuDNN, CuBLAS.

Responsibilities

• Optimize model training and inference performance at scale.
• Improve real-time and high-throughput inference in large systems.
• Collaborate with ML and systems teams to deliver end-to-end optimization.

Skills

ML techniques

End-to-end perf debugging

Low-level GPU knowledge

CUDA debugging tools

cuDNN/cuBLAS familiarity

NCCL/MPI distributed training

Networking tech (Infiniband/NVLink)

English fluency

Tools

CUDA GDB

Nsight Systems

Nsight Compute

Triton

CUTLASS

CUB

Thrust

cuDNN

cuBLAS

Infiniband

RoCE

GPUDirect

NVLink

NCCL

MPI

Job 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?

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