Member of Technical Staff, ML Systems

About the Role

Join an ML systems team building and optimizing training and inference infrastructure for image, video, diffusion, and world-model workloads. You will improve performance across the stack, from GPU kernels to distributed training and serving.

What You'll Do

• Optimize training and inference performance for image, video, diffusion, 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 using NCCL, RDMA, InfiniBand, or RoCE.

• Build benchmarks and regression tests to preserve performance gains in production.

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

What We're Looking For

• At least 2 years of experience in deep learning training or inference systems, GPU performance, or distributed systems, including 1 or more years of hands-on ML systems or GPU performance work in a recent role.

• A degree in computer science or a related quantitative field, with strong ML and computer science fundamentals.

• Hands-on experience authoring GPU kernels directly for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.

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

• Familiarity with PyTorch, Nsight, NCCL, and RDMA, and experience optimizing image, video, diffusion, or other multimodal workloads.

Compensation & Benefits

Base salary: $180,000 to $230,000 annually. Visa sponsorship is available.

Location

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