Jane Street
Machine Learning Performance EngineerNew York, New York, United States
Jane Street, New York, New York, United States, 10001
We are looking for an engineer with experience in low-level systems programming and optimization 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 optimizing 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 youve never thought about a career in finance, youre 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 youll fit right in.
Theres no fixed set of skills, but here are some of the things were looking for:
An understanding of modern ML techniques and toolsets
The experience and systems knowledge required to debug a training runs performance end to end
Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores, and the memory hierarchy
Debugging and optimization experience using tools like CUDA GDB, NSight Systems, 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 optimization, 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
If you're a recruiting agency and want to partner with us, please reach out to
agency-partnerships@janestreet.com
.
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 optimizing 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 youve never thought about a career in finance, youre 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 youll fit right in.
Theres no fixed set of skills, but here are some of the things were looking for:
An understanding of modern ML techniques and toolsets
The experience and systems knowledge required to debug a training runs performance end to end
Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores, and the memory hierarchy
Debugging and optimization experience using tools like CUDA GDB, NSight Systems, 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 optimization, 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
If you're a recruiting agency and want to partner with us, please reach out to
agency-partnerships@janestreet.com
.