Member of Technical Staff, ML Systems

ABOUT THE ROLE Join a team building the training and inference stack for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and serving systems. WHAT YOU'LL DO - Optimize training and inference performance for image, video, and world-model workloads. - Profile kernel, memory, system, and cluster bottlenecks using Nsight and related tools. - Implement low-level GPU optimizations with CUDA and Triton. - Design and improve distributed training and inference engines for diffusion models. - Improve communication performance across GPUs and nodes, including NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving. - Build benchmarks and regression tests to sustain performance improvements in production. - Apply hardware-aware co-design across kernels, runtimes, and models. WHAT WE'RE LOOKING FOR - At least 2 years of experience in deep learning training or inference systems, distributed systems, or related ML systems work. - At least 1 year of recent, hands-on experience with ML systems or GPU performance. - Experience authoring core features in an inference or training framework, rather than focusing only on deployment or integration. - Hands-on experience writing GPU kernels for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX. - Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field. - Experience optimizing inference or training performance, including GPU kernels, runtimes, or distributed execution. - Experience with diffusion, video, image, or other multimodal workloads is valuable. COMPENSATION & BENEFITS Base salary: $180,000 to $260,000 annually. Visa sponsorship is available. LOCATION On-site in Menlo Park, California, United States.