About the Role

Join a team building high-performance training and inference systems for image, video, and world-model workloads. You will improve speed and efficiency across GPU kernels, distributed execution, and multi-node serving.

What You'll Do

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

Profile and remove 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, RoCE, and disaggregated serving approaches.

Build benchmarks and regression tests to sustain performance improvements in production.

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

What We're Looking For

Two or more years of experience in deep learning training or inference systems, distributed systems, or related ML systems work.

At least one year of recent, hands-on experience with ML systems or GPU performance.

Experience authoring core features in a training or inference framework, rather than deployment or integration work alone.

Hands-on GPU kernel development for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX, including writing kernels directly.

Experience improving inference or training performance across kernels, runtimes, or distributed execution.

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

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

Compensation & Benefits

Annual base salary: $180,000 to $260,000. Visa sponsorship is available.

Location

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