Machine Learning Performance Engineer

Machine Learning Performance Engineer

Requirements

• 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 optimization 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 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

Responsibilities

• Optimizing the performance of models—both training and inference
• Improving straightforward CUDA
• Ensuring the platform makes sense at the lowest level
• Diving deep into market data
• Tuning hyperparameters
• Debugging distributed training performance
• Studying how the model likes to trade in production

Preferred Qualifications

No preferred qualifications provided.