Member of Technical Staff, ML Systems

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 infrastructure.

What You'll Do

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

• Profile 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 technologies such as NCCL, RDMA, InfiniBand, and RoCE.

• Build benchmarking and regression tools to maintain performance gains in production.

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

What We're Looking For

• At least 2 years of experience in deep learning training or inference systems, distributed systems, or related ML systems work.

• Hands-on GPU performance experience, including at least 1 year in a full-time role focused on ML systems or GPU performance.

• Experience authoring core features in a training or inference framework, rather than only deploying or integrating one.

• Direct NVIDIA GPU kernel development experience with CUDA, CUTLASS, Triton, or PTX.

• Strong machine learning and computer science fundamentals, with 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 diffusion, video, image, or other multimodal workloads is relevant.

Compensation & Benefits

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

Location

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