Member of Technical Staff, ML Systems

About the Role

Join an AI/ML team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across the stack, from GPU kernels to distributed, multi-node systems.

What You'll Do

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

• Profile and address bottlenecks across GPU kernels, memory, systems, and clusters using tools such as Nsight.

• Implement low-level optimizations in 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 help sustain performance improvements in production.

• Co-design kernels, runtimes, and models with hardware capabilities 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 experience with GPU performance or ML systems in a recent role, including work on inference or training performance.

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

• Directly written kernels for NVIDIA GPUs 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 diffusion, video, image, or other multimodal workloads is valuable.

• Experience with PyTorch, Nsight, NCCL, and RDMA is relevant to the role.

Compensation & Benefits

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

Location

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