GPU
ABOUT THE ROLE
Join an AI systems team rebuilding 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 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 GPU optimizations in CUDA and Triton.
- Design and improve distributed training and inference engines for diffusion models.
- Improve communication performance across GPUs and nodes using NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving approaches.
- Build benchmarks and regression tests to preserve performance gains in production.
- Co-design kernels, runtimes, and models with hardware characteristics 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, such as vLLM, SGLang, TensorRT-LLM, Megatron, or a comparable system.
- Direct experience writing GPU kernels for NVIDIA hardware 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 optimi