Description
Systems Engineering is an engineering discipline focused on building, automating, and operating the platforms and tooling that deliver large-scale production systems with high efficiency, reliability, and velocity. It combines software and systems engineering practices across infrastructure automation, containerized platforms, storage, telemetry, and observability.
Our team at NVIDIA ensures that our internal and external facing GPU cloud services are deployed reliably, observable end-to-end, and continuously improved through automation. We enable developers to ship changes safely through repeatable CI/CD pipelines and Kubernetes-based deployments while keeping an eye on capacity, latency, and performance.
Design, deploy, and operate solutions on Kubernetes for large-scale storage and data platforms, including the manifests, Helm charts, and operators that run them.
Build tools, services, and automation that improve the lifecycle of storage and data systems – from provisioning and configuration through deployment, scaling, and day-2 operations.
Develop and operate telemetry and observability for production systems – metrics, logging, tracing, dashboards, and alerting – so that system health, availability, and latency are measurable and actionable.
Apply strong analytical troubleshooting skills to diagnose and resolve complex issues across distributed, containerized infrastructure.
Work closely with peers and partner teams to improve the lifecycle of services, from inception and design through deployment, operation, and refinement.
Scale systems sustainably through automation, infrastructure-as-code, and CI/CD, and evolve systems by pushing for changes that improve reliability and velocity.
Support services before they go live through activities such as deployment automation, capacity planning, and launch and readiness reviews.
Practice sustainable incident response and postmortems, and participate in an on-call rotation to support production systems.