Python · C++ · Java · TensorFlow · Keras
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, r