Member of Technical Staff, ML Systems

About the Role

Join an AI/ML team building and optimizing the training and inference stack for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed engines, and multi-node systems, helping make demanding model 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 using 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 ensure performance improvements persist in production.

• Co-design kernels, runtimes, and models with target hardware 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, with 1 or more years of hands-on GPU performance experience in a recent role.

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

• Direct experience writing GPU kernels for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX.

• Strong computer science and machine learning fundamentals, with a degree in computer science or a related quantitative field.

• Experience with PyTorch, Nsight, NCCL, and RDMA, and performance optimization for inference or training.

• Background optimizing diffusion, video, image, or other multimodal workloads is valuable.

Compensation & Benefits

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

Location

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