Remote Cuda Developer Jobs in Austin, TX (NOW HIRING)

Title: Machine Learning Performance Engineer - CUDA Python

Work Authorization – USC / GC only

Interview: Video

Duration:  6-month contract maybe extensions  

Location:  50% travel

Duration: 6 month contract

*Must be willing to travel 50% of the time

*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 PTX, SASS, warps, cooperative groups, Tensor Cores, 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