AI · GPU kernels
ABOUT THE ROLE
Join a technical team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed engines, and multi-node infrastructure, helping make demanding AI workloads faster and more efficient.
WHAT YOU'LL DO
- Optimize system and GPU performance for training and inference across image, video, and world-model workloads.
- Profile 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.
- Build benchmarking and regression harnesses to sustain performance improvements in production.
- Co-design kernels, runtimes, and models with hardware constraints in mind.
WHAT WE'RE LOOKING FOR
- At least 2 years of experience with deep learning training or inference systems, distributed systems, or related ML systems work.
- At least 1 year of hands-on ML systems or GPU performance experience in a full-time role.
- Experience authoring core features in a training or inference framework, rather than only deploying or integrating one.
- Directly written NVIDIA GPU kernels using CUDA, CUTLASS, Triton, or PTX.
- Experience improving inference or training performance through kernels, runtimes, or distributed execution.
- Strong computer science and machine learning f