training · inference · GPU kernels
ABOUT THE ROLE
Join a team building the training and inference stack for image, video, and world-model workloads. You will improve performance across GPU kernels, distributed execution, and serving systems.
WHAT YOU'LL DO
- Optimize training and inference performance for image, video, and world-model workloads.
- Profile kernel, memory, system, and cluster bottlenecks 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, including NCCL, RDMA, InfiniBand, RoCE, and disaggregated serving.
- Build benchmarks and regression tests to sustain performance improvements in production.
- Apply hardware-aware co-design across kernels, runtimes, and models.
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.
- At least 1 year of recent, hands-on experience with ML systems or GPU performance.
- Experience authoring core features in an inference or training framework, rather than focusing only on deployment or integration.
- Hands-on experience writing GPU kernels for 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 optimizing inference or training performance, including GPU kernels, runtimes, or distributed execution.
- Experience