Machine Learning Performance Engineer

Jane Street; London; On-site; GBP 60,000 - 90,000; Full time

Job summary

Jane Street is seeking an engineer with expertise in low-level systems programming to enhance the performance of machine learning models. In this role, you will work with complex systems to ensure efficient training, low-latency inference, and overall optimal performance in a fast-paced trading environment.

Qualifications

• Experience with low-level systems programming and optimisation.
• Familiarity with CUDA, PTX, SASS, and Tensor Cores.
• Strong debugging experience with CUDA tools.

Responsibilities

• Optimising the performance of models in a real-time trading environment.
• Ensuring efficient large-scale training and low-latency inference.
• Improving CUDA performance and addressing system-level considerations.

Skills

Understanding of modern ML techniques

Experience and systems knowledge for debugging performance

Low-level GPU knowledge

Debugging and optimisation with CUDA tools

Library knowledge of Triton and cuDNN

Intuition about CUDA characteristics

Background in Infiniband and NVLink

Understanding of collective algorithms in NCCL or MPI

Inventive approach to problem-solving

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

Accepted file types: pdf, doc, docx, txt, rtf

Enter manually

Accepted file types: pdf, doc, docx, txt, rtf

Additional information (for source)

Have you interviewed with Jane Street before? * Select...

Pronouns

Are you currently a student? * Select...

Enter your college/university

Major/Field of study

Extra

Why you’re interested in Jane Street
How you heard about us
If you have a job now, why you’re looking for a new one

Year you expect to begin full time employment