Member of Technical Staff, ML Systems

Role details

ABOUT THE ROLE Join an AI systems team building high-performance training and inference infrastructure for image, video, and world-model workloads. You will improve speed and efficiency 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 bottlenecks at kernel, memory, system, and cluster levels 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 using NCCL, RDMA, InfiniBand, or RoCE. - Build benchmarking and regression tools to maintain performance gains in production. - Coordinate hardware, runtime, kernel, and model design for efficient execution. WHAT WE'RE LOOKING FOR - At least 2 years of experience in deep learning training or inference systems, or distributed systems, including hands-on ML systems or GPU performance work. - Experience optimizing inference or training performance through GPU kernels, runtime systems, or distributed execution. - Direct experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX. - Authored core features in a training or inference framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron, rather than focusing only on deployment or integration. - Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field. - Experience with PyTorch, Nsight, and workloads such as diffusion, video, image, or other multimodal models is relevant. COMPENSATION & BENEFITS Base salary: $180,000 to $230,000 annually. Visa sponsorship is available. LOCATION On-site in Menlo Park, California, United States.