GPU
ABOUT THE ROLE
Join an AI team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed runtimes, and multi-node systems, helping make demanding workloads faster and more efficient.
WHAT YOU'LL DO
- Optimize training and inference performance for image, video, and world-model workloads.
- Profile and address bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools.
- Implement low-level optimizations with CUDA and Triton.
- Design and improve distributed training and inference engines for diffusion models.
- Improve communication performance across GPUs and nodes using NCCL, RDMA, InfiniBand, and RoCE.
- Build benchmarking and regression tools to help preserve performance improvements in production.
- Co-design kernels, runtimes, and models with hardware characteristics in mind.
WHAT WE'RE LOOKING FOR
- At least 2 years of experience in deep learning training or inference systems, GPU performance, or distributed systems, including 1 or more years of hands-on ML systems or GPU performance work in a recent role.
- Experience authoring core features in an inference or training framework such as vLLM, SGLang, TensorRT-LLM, or Megatron, rather than focusing only on deployment or integration.
- Hands-on experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.
- Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field.
- Experienc