GPU · image
ABOUT THE ROLE
Join an AI company building systems for training and serving image, video, and world-model workloads. You will improve performance across the stack, from GPU kernels to distributed training and inference engines.
WHAT YOU'LL DO
- Optimize training and inference performance for image, video, and world-model workloads.
- Profile and resolve 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 benchmarks and regression tests to preserve performance improvements in production.
- Collaborate on hardware-aware kernel, runtime, and model design.
WHAT WE'RE LOOKING FOR
- Two or more years of experience in deep learning training or inference systems, distributed systems, or related ML systems work.
- At least one year of recent, hands-on experience in ML systems or GPU performance engineering.
- Experience authoring core features in an inference or training framework, such as vLLM, SGLang, TensorRT-LLM, or Megatron.
- Hands-on experience writing kernels for NVIDIA GPUs 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 across GPU kernels, runtimes, or distributed