Member of Technical Staff, ML Systems

ABOUT THE ROLE Join a team building high-performance training and inference systems for image, video, and world-model workloads. You will improve speed and efficiency across GPU kernels, distributed execution, and multi-node serving. WHAT YOU'LL DO - Optimize training and inference performance for image, video, and world-model workloads. - Profile and remove 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 benchmarks and regression tests to sustain performance improvements in production. - Co-design kernels, runtimes, and models with hardware capabilities in mind. 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 recent, hands-on experience with ML systems or GPU performance. - Experience authoring core features in a training or inference framework, rather than deployment or integration work alone. - Hands-on GPU kernel development for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX, including writing kernels directly. - Experience improving inference or training performance across kernels, runtimes, or distributed execution. - Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field. - Experience optimizing diffusion, video, image, or other multimodal workloads is valuable. COMPENSATION & BENEFITS Annual base salary: $180,000 to $260,000. Visa sponsorship is available. LOCATION On-site in Menlo Park, California, United States.