Member of Technical Staff, ML Systems

About the Role

Join a technical team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed engines, and multi-node infrastructure, helping make demanding AI workloads faster and more efficient.

What You'll Do

• Optimize system and GPU performance for training and inference across 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 NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving.

• Build benchmarking and regression harnesses to sustain performance improvements in production.

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

What We're Looking For

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

• At least 1 year of hands-on ML systems or GPU performance experience in a full-time role.

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

• Directly written NVIDIA GPU kernels using CUDA, CUTLASS, Triton, or PTX.

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

• Strong computer science and machine learning 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

Visa sponsorship is available.

Location

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