Quick overview
Job description
About the Role
Join an AI/ML team building and optimizing 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 system and GPU performance for training and inference across 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 using 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 ensure performance improvements persist in production.
Co-design kernels, runtimes, and models with 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, with 1 or more years of hands-on GPU performance experience in a recent role.
Experience authoring core features in an inference or training framework, rather than focusing only on deployment or integration.
Direct experience writing GPU kernels for NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX.
Strong computer science and machine learning fundamentals, with a degree in computer science or a related quantitative field.
Experience with PyTorch, Nsight, NCCL, and RDMA, and performance optimization for inference or training.
Background optimizing diffusion, video, image, or other multimodal workloads is valuable.
Compensation & Benefits
Base salary is $180,000 to $260,000 annually. Visa sponsorship is available.
Location
On-site in Menlo Park, California, United States.