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 Compute
Qualification
Library knowledge of TritonBackground in Infiniband
Required
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
