ML Performance Engineer with CUDA and Python

ML Performance Engineer CUDA Python

Duration: 6-month contract with the likelihood of extending

*Must be willing to travel
*Must have strong pre-sales abilities i.e. presentation skills, communication skills, etc.
*Must be willing to help train employees and customers

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?

• • 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 , 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