About the Role

Join an AI team building high-performance training and inference systems for image, video, and world-model workloads. You will improve efficiency across GPU kernels, distributed engines, and multi-node systems, helping make advanced models faster to train and serve.

What You'll Do

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

Use Nsight and related tools to identify and remove kernel, memory, system, and cluster bottlenecks.

Implement low-level optimizations using CUDA and Triton.

Design and improve distributed inference and training engines for diffusion models.

Improve communication performance across GPUs and nodes using NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving techniques.

Build benchmarking and regression harnesses to maintain performance gains 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 full-time role.

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

Hands-on experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.

Strong machine learning and computer science fundamentals, supported by a degree in computer science or a related quantitative field.

Experience optimizing inference or training performance, including GPU kernels, runtimes, or distributed execution.

Experience with PyTorch, Nsight, NCCL, and RDMA, and familiarity with diffusion, video, image, or other multimodal workloads.

Compensation & Benefits

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

Location

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