Member of Technical Staff, ML Systems

Join an AI/ML team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across the stack, from GPU kernels to distributed, multi-node systems.

• Optimize training and inference performance for image, video, and world-model workloads.

• Profile and resolve bottlenecks across kernels, memory, systems, and clusters 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, RoCE, and disaggregated serving.

• Build benchmarking and regression tools to help sustain performance improvements in production.

• Co-design kernels, runtimes, and models with hardware capabilities in mind.

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

• At least 2 years of experience in deep learning training or inference systems, distributed systems, or related ML systems work, including hands-on performance work in a recent role.

• Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron.

• Direct experience writing GPU kernels on 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 with PyTorch, Nsight, NCCL, and RDMA is relevant to the work.

• Background optimizing diffusion, video, image, or other multimodal workloads is valuable.

Base salary: $180,000 to $260,000 annually. Visa sponsorship is available.