Member of Technical Staff, ML Systems

ABOUT THE ROLE Join an AI/ML systems team building efficient training and inference infrastructure for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and multi-node serving systems. WHAT YOU'LL DO - Optimize training and inference performance for 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 in CUDA and Triton. - Design and improve distributed training and inference engines for diffusion models. - Improve communication performance across GPUs and nodes using technologies such as NCCL and RDMA. - Build benchmarks and regression tests to preserve performance improvements in production. - Co-design kernels, runtimes, and models with the target hardware in mind. 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. - Hands-on experience improving inference or training performance, including GPU kernels, runtimes, or distributed execution. - Experience authoring core features in an inference or training framework, rather than focusing only on deployment or integration. - Direct experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX. - Strong machine learning and computer science fundamentals, supported by a degree in computer science or a related quantitative field. - Experience optimizing 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.