Python · GPU
ABOUT THE ROLE
Join an AI/ML team building efficient training and inference systems for image, video, and world-model workloads. You will improve performance across the stack, from GPU kernels to distributed, multi-node systems.
WHAT YOU'LL DO
- Optimize training and inference performance for image, video, and world-model workloads.
- Profile and resolve bottlenecks across kernels, memory, systems, and clusters using Nsight and related tools.
- Implement low-level GPU optimizations with 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.
- Build benchmarking and regression tools to help sustain performance improvements in production.
- Co-design kernels, runtimes, and models with hardware capabilities 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, including hands-on performance work in a recent role.
- Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron.
- Direct experience writing GPU kernels on NVIDIA hardware using CUDA, CUTLASS, Triton, or PTX.
- Strong machine learning and computer science fundamentals, with a degree in computer science or a related quantitative field.
- Experience with PyTorch, Nsight, NCCL, and RDMA is relevant to the work.
- Background optimizing diffusion, video, imag