Senior Member of Technical Staff, ML Systems

About the Role

Join an AI systems team building 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 training and inference performance for image, video, diffusion, and world-model workloads.

• Use Nsight and related tools to identify bottlenecks across kernels, memory, systems, and clusters.

• 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 track performance improvements in production.

• Co-design kernels, runtimes, and models with hardware constraints 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 hands-on performance work in a recent role.

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

• Direct experience writing CUDA, CUTLASS, Triton, or PTX kernels for NVIDIA GPUs.

• Experience contributing core features to an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron, rather than focusing only on deployment or integration.

• Background in GPU kernels, runtimes, or distributed execution, ideally with diffusion, video, image, or other multimodal workloads.

Compensation & Benefits

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

Location

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