Member of Technical Staff, ML Systems

About the Role

Join an AI/ML team building high-performance training and inference systems for image, video, and world-model workloads. You will improve speed and efficiency across GPU kernels, distributed execution, and multi-node serving.

What You'll Do

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

• Build benchmarks and regression tests to ensure performance improvements hold 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, distributed systems, or related ML systems work.

• At least 1 year of hands-on ML systems or GPU performance experience in a current or recent role.

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

• Hands-on 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 valuable.

Compensation & Benefits

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

Location

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