Member of Technical Staff, ML Systems

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.