Member of Technical Staff, ML Systems

About the Role

Join an AI/ML 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 multi-node serving systems.

What You'll Do

• Optimize training and inference performance for image, video, and world-model workloads.
• Profile bottlenecks at the kernel, memory, system, and cluster levels using Nsight and related tools.
• Implement low-level optimizations in 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 and RDMA.
• Build benchmarks and regression tests to preserve performance improvements in production.
• Co-design kernels, runtimes, and models with the target hardware 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.
• Hands-on experience improving inference or training performance, including GPU kernels, runtimes, or distributed execution.
• Experience authoring core features in an inference or training framework, rather than focusing only on deployment or integration.
• Direct experience writing kernels for NVIDIA GPUs using CUDA, CUTLASS, Triton, or PTX.
• Strong machine learning and computer science fundamentals, supported by a degree in computer science or a related quantitative field.
• Experience optimizing diffusion, video, image, or other multimodal workloads is valuable.

Compensation & Benefits

Base salary

$180,000 to $260,000 annually. Visa sponsorship is available.

Location

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