Senior Member of Technical Staff, ML Systems

About the Role

Join an AI infrastructure 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 serving, helping make demanding ML workloads faster and more efficient.

What You'll Do

• Optimize GPU and system performance for training and inference workloads.

• Profile and address bottlenecks at the kernel, memory, system, and cluster levels 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 technologies such as NCCL, RDMA, InfiniBand, and RoCE.

• Build benchmarking and regression tools to maintain performance gains in production.

• Co-design kernels, runtimes, and models with hardware performance 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, including at least 1 year of hands-on ML systems or GPU performance work.

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

• Hands-on NVIDIA GPU kernel development with CUDA, CUTLASS, Triton, or PTX, and experience with PyTorch and Nsight.

• Authorship of core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron. Deployment or integration work alone is not sufficient.

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

• Experience with multi-GPU or multi-node communication, such as NCCL, RDMA, InfiniBand, or RoCE.

• Experience with diffusion, video, image, or other multimodal workloads is useful. The role focuses on direct kernel and ML systems work rather than compiler-only work.

Compensation & Benefits

Annual salary range: $180,000 to $230,000 USD. Visa sponsorship is available.

Location

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