Member of Technical Staff, ML Systems

About the Role

Join an AI systems team building efficient training and inference infrastructure for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and serving systems.

What You'll Do

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

• Profile bottlenecks across kernels, memory, systems, and clusters using Nsight and related tools.

• Implement low-level 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, and RoCE.

• Build benchmarking and regression tools to preserve 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, or distributed systems.

• Hands-on ML systems or GPU performance experience, including at least 1 year in a current or recent role.

• Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron.

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

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

• Experience optimizing inference or training performance, including GPU kernels, runtimes, or distributed execution.

• Experience with diffusion, video, image, or other multimodal workloads is relevant.

Compensation & Benefits

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

Location

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