Member of Technical Staff, ML Systems

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 execution, and multi-node serving.

What You'll Do

• Optimize system and GPU performance for training and inference across 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, RoCE, and disaggregated serving approaches.

• Build benchmarking and regression harnesses to preserve performance gains in production.

• Collaborate on hardware-aware kernel, runtime, and model design.

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 hands-on ML systems or GPU performance work in a current or recent role.

• Experience authoring core features in a training or inference framework, rather than focusing only on deployment or integration.

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

• Experience improving inference or training performance across GPU kernels, runtime systems, 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 relevant.

Compensation & Benefits

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

Location

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