Member of Technical Staff, ML Systems

About the Role

Join a team building the training and inference stack for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and serving systems.

What You'll Do

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

• Profile kernel, memory, system, and cluster bottlenecks using Nsight and related tools.

• Implement low-level GPU optimizations with CUDA and Triton.

• Design and improve distributed training and inference engines for diffusion models.

• Improve communication performance across GPUs and nodes, including NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving.

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

• Apply hardware-aware co-design across kernels, runtimes, and models.

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.

• At least 1 year of recent, hands-on experience with ML systems or GPU performance.

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

• Hands-on experience writing GPU kernels for NVIDIA hardware using 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 valuable.

Compensation & Benefits

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

Location

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