Quick overview
Job description
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, image, or other multimodal workloads is valuable.
Compensation & Benefits
Base salary: $180,000 to $260,000 annually. Visa sponsorship is available.
Location
On-site in Menlo Park, California, United States.