Member of Technical Staff

Description

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.

Responsibilities

• 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.

Requirements

• 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 optimizing training or inference performance across GPU kernels, runtimes, or distributed execution.
• Experience with PyTorch, Nsight, NCCL, and RDMA, and ideally optimization of diffusion, video, image, or other multimodal workloads.

Conditions

Base salary: $180,000 to $260,000 annually. Visa sponsorship is available.

On-site in Menlo Park, California, United States.