Machine Learning Performance Engineer
Jane Street2 months ago
London, United KingdomSenior
Responsibilities
- Optimize machine-learning model training performance.
- Optimize low-latency inference for real-time systems.
- Optimize high-throughput inference for research.
- Improve CUDA performance using a whole-systems approach across storage, networking, host systems, and GPUs.
- Debug training-run performance end to end and assess throughput and goodput at low system levels.
- Improve the efficiency of distributed GPU training and GPU-cluster networking.
Requirements
- Understanding of modern machine-learning techniques and toolsets.
- Experience and systems knowledge sufficient to debug training-run performance end to end.
- Low-level GPU knowledge including PTX, SASS, warps, cooperative groups, Tensor Cores, and memory hierarchy.
- Experience debugging and optimizing with CUDA GDB, Nsight Systems, and Nsight Compute.
- Knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN, and cuBLAS.
- Understanding of CUDA graph launch, tensor-core arithmetic, warp-level synchronization, and asynchronous memory loads.
- Background in InfiniBand, RoCE, GPUDirect, PXN, rail optimization, and NVLink for connecting GPU clusters.
- Understanding of collective algorithms for distributed GPU training in NCCL or MPI.
- Inventive problem-solving ability and willingness to question technical approaches and tool choices.
- Fluency in English.
Tech Stack
Sass
Categories
About Jane Street
Jane Street is a global quantitative trading firm and liquidity provider that builds in-house software and research platforms to trade across asset classes. It makes markets and executes proprietary strategies on exchanges and electronic venues, serving institutional markets rather than individual investors. Founded in 2000 and headquartered in New York, it is privately held with offices in London, Hong Kong, Singapore, and Amsterdam.