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About the Role
We are seeking an engineer with expertise in low-level systems programming and optimization to enhance the performance of our machine learning models, focusing on both training and inference. This role involves a systems-level approach, considering storage, networking, and host/GPU factors to ensure efficient and effective model execution.
Responsibilities
- Optimizing the performance of machine learning models, including training and inference.
- Improving CUDA performance.
- Applying a whole-systems approach to optimization, including storage systems, networking, and host- and GPU-level considerations.
- Ensuring platform efficiency at the lowest level, analyzing throughput versus goodput and cache performance.
- Debugging performance issues end-to-end.
- Utilizing networking technologies to link GPU clusters.
- Understanding and applying collective algorithms for distributed GPU training.
Requirements
- Experience in low-level systems programming and optimization.
- Systems knowledge to debug training run performance end-to-end.
- Low-level GPU knowledge including PTX, SASS, warps, cooperative groups, Tensor Cores, and the memory hierarchy.
- Debugging and optimization experience using tools like CUDA GDB, NSight Systems, NSight Compute.
- Background in Infiniband, RoCE, GPUDirect, PXN, rail optimization, and NVLink.
- Understanding of collective algorithms supporting distributed GPU training in NCCL or MPI.
- An inventive approach and willingness to question existing methods and tools.
Skills
- Modern ML techniques and toolsets
- Low-level GPU programming
- CUDA GDB
- NSight Systems
- NSight Compute
- Triton
- CUTLASS
- CUB
- Thrust
- cuDNN
- cuBLAS
- CUDA graph launch
- Tensor core arithmetic
- Warp-level synchronization
- Asynchronous memory loads
- Infiniband
- RoCE
- GPUDirect
- PXN
- Rail optimization
- NVLink
- NCCL
- MPI
About the Company
- 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.
- 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.