machine learning · GPU kernels
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 execution, and multi-node serving.
WHAT YOU'LL DO
- Optimize system and GPU performance for training and inference across 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, RoCE, and disaggregated serving approaches.
- Build benchmarking and regression harnesses to preserve performance gains in production.
- Collaborate on hardware-aware kernel, runtime, and model design.
WHAT WE'RE LOOKING FOR
- Two or more years of experience in deep learning training or inference systems, distributed systems, or related ML systems work.
- At least one year of hands-on ML systems or GPU performance work in a current or recent role.
- Experience authoring core features in a training or inference framework, rather than focusing only on deployment or integration.
- Direct GPU kernel development experience with CUDA, CUTLASS, Triton, or PTX on NVIDIA GPUs.
- Experience improving inference or training performance across GPU kernels, runtime systems, or distributed execution.
- Strong machine learning and computer science fundamentals, with a degree