Machine Learning Performance Engineer

Quant Blueprint LLC; Greater London; On-site; GBP 50,000 - 70,000; Full time

Job summary

Quant Blueprint LLC is seeking an engineer proficient in low-level systems programming and optimization to enhance our machine learning team. This role centers on optimizing model performance, both for training and real-time inference. Applicants should have experience with modern ML techniques, GPU knowledge, and debugging skills in CUDA.

The ideal candidate will thrive in a dynamic atmosphere with a collaborative mindset and strong problem-solving abilities. A solid understanding of networking technologies and collective algorithms is essential.

Qualifications

• Experience debugging a training run's performance end to end.
• Optimisation experience with high-throughput inference.
• Background in networking technologies like Infiniband and NVLink.

Responsibilities

• Optimise the performance of ML models during training and inference.
• Ensure effective large-scale training and low-latency inference.
• Ask critical questions about current approaches and tools.

Skills

Understanding of modern ML techniques and toolsets

Low-level GPU knowledge of PTX, SASS, warps

Debugging experience using tools like CUDA GDB

Library knowledge of Triton, cuDNN

Fluency in English

Tools

CUDA GDB

NSight Systems

NCCL

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.

What 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 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.