Teams at NVIDIA

Build and scale software infrastructure for GPU asset provisioning, data center management, monitoring, lifecycle automation, and distributed system testing. Develop reliable platforms across hardware, networking, cloud providers, and AI training applications. Work with GPU diagnostics, cluster and network telemetry, Kubernetes or SLURM, fault tolerance, state management, and system architecture to support large-scale machine learning infrastructure.

Designs, builds, maintains, and deploys large-scale HPC and AI hybrid computing solutions for NVIDIA partners and customers. Guides adoption of end-to-end Agentic AI solutions using NVIDIA compute, networking, and software stacks. Provides technical leadership through demonstrations, proofs of concept, documentation, and debugging while collaborating with executives and engineering teams. Requires expertise in cluster management, containers, Kubernetes, Slurm, Ansible, networking, storage, and cloud-based infrastructure.

Act as a technical advisor to academic AI labs working on foundational models. Identify research workloads suited to NVIDIA platforms, engage researchers to remove infrastructure blockers, track frontier research, partner with internal teams to drive adoption, translate academic feedback into product and program improvements, and support NVIDIA participation at major research venues through content, workshops, and lab programs.