Member of Technical Staff, ML Systems

Join an AI and machine learning team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and multi-node serving, helping make demanding model workloads faster and more efficient.

• Optimize training and inference performance for image, video, diffusion, and world-model workloads.
• Profile bottlenecks across kernels, memory, systems, and clusters using Nsight and related tools.
• Implement low-level GPU optimizations using 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, RDMA, Infini Band, and RoCE.
• Build benchmarks and regression tests to ensure performance improvements hold in production.
• Contribute to hardware-aware co-design across kernels, runtimes, and models.

• At least 2 years of experience in deep learning training or inference systems, distributed systems, or related work, with hands-on ML systems or GPU performance experience.
• Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, Megatron, or a similar system.
• Hands-on experience writing GPU kernels for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX.
• Strong machine learning and computer science fundamentals, typically supported by a degree in computer science or a related quantitative field.
• Experience with PyTorch, Nsight, NCCL, and RDMA is relevant.
• Background optimizing inference or training performance, ideally for diffusion, video, image, or other multimodal workloads.

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

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