Quick overview
Job description
About the Role
Join an AI team building high-performance training and inference systems for image, video, and world-model workloads. You will improve efficiency across GPU kernels, distributed engines, and multi-node infrastructure.
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 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 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.
Hands-on GPU performance experience, including at least 1 year in a full-time role focused on ML systems or GPU performance.
Experience authoring core features in a training or inference framework, rather than only deploying or integrating one.
Direct NVIDIA GPU kernel development experience with CUDA, CUTLASS, Triton, or PTX.
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 is $180,000 to $260,000 annually. Visa sponsorship is available.
Location
On-site in Menlo Park, California, United States.