About the Role

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

What You'll Do

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

Profile and address bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools.

Implement low-level 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, and RoCE.

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

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

What We're Looking For

At least 2 years of experience in deep learning training or inference systems, GPU performance, or distributed systems, including 1 or more years of hands-on ML systems or GPU performance work in a recent role.

Experience authoring core features in an inference or training framework such as vLLM, SGLang, TensorRT-LLM, or Megatron, rather than focusing only on deployment or integration.

Hands-on experience writing kernels for NVIDIA GPUs 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, and performance work on training or inference systems.

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

Compensation & Benefits

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

Location

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