Teams at NVIDIA

Develop and deploy AI infrastructure solutions across NVIDIA cloud, partner, and hyperscale environments. Build distributed training, inference, and MLOps pipelines; manage Kubernetes-based GPU workloads; optimize performance, cost, and reliability; and implement observability and SLO/SLA monitoring. Provide hands-on technical support to strategic customers and partners, troubleshoot production issues, integrate enterprise systems, and create implementation guides, runbooks, and postmortems.

Design, build, and operate Go and Python tooling that validates GPU clusters across Kubernetes and Slurm. Develop certification systems, benchmarks, workload catalogs, provenance and evidence pipelines, platform integrations, and signed auditable results. Investigate performance and correctness issues across distributed AI infrastructure, contribute to open-source projects, and author technical documentation. The role requires deep Linux, container, Kubernetes, HPC, GPU, and distributed-systems expertise, with collaboration across internal teams and cloud partners.

Build and operate reliable full-stack features for NVIDIA’s AI Hub, spanning frontend, backend, and data layers. Own features from design through production support, improve system reliability and observability, collaborate with product and AI research stakeholders, participate in reviews and on-call rotations, and apply modern cloud infrastructure, deployment, testing, and monitoring practices.