AI/ML systems · image · video · world-model workloads
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 multimo