Member of Technical Staff, ML Systems

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.

• 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, Infini Band, or RoCE.
• Build benchmarking and regression tools to maintain performance gains in production.
• Coordinate hardware, runtime, kernel, and model design for efficient execution.

• 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.

Base salary: $180,000 to $230,000 annually. Visa sponsorship is available.

On-site in Menlo Park, California, United States.