NVIDIA is hiring engineers to build and operate the governed infrastructure platform behind our Electronic Design Automation (EDA) and hardware engineering organization, giving teams a self-service path from source code to production that runs in the cloud and at each physical site. We need engineers with strong programming skills and a deep understanding of distributed systems. You should have real experience operating large-scale production infrastructure and the judgment to build a platform capability once, for every team that needs it.
The ideal candidate is comfortable working across the cloud-native stack, from Kubernetes down to cluster schedulers. When a new capability is needed, you look for an existing open-source solution before writing your own. What you do build gets built once on the platform, for every team to use. You enjoy solving complex operational problems and turning manual handoffs into self-service, and you care about developer experience as much as you care about uptime. You want systems that stay reliable as the fleet and the number of onboarded teams grow. If that sounds like you, we would like to hear from you.
What You Will Be Doing:
Own the lifecycle of the Kubernetes clusters the platform runs on, including the control plane, node lifecycle, and fleet-wide upgrades. Own the bare-metal provisioning, storage, and networking underneath them too.
Build self-service capabilities (compute, secrets, certificates, DNS, access, deployment) so engineering teams can declare what they need and get it automatically.
Extend Kubernetes with controllers and operators, using open-source projects where they fit and writing your own where they don’t.
Own the CI/CD pipelines and shared platform services, such as policy, autoscaling and observability, that give teams a secure path to production by default.
Onboard new teams and sites onto the platform as a repeatable motion. Define what a site or team needs to bring online, then build the self-service tooling and documentation that gets them there and lets them operate independently afterward.
Work directly with EDA, infrastructure, networking, storage, and hardware engineering teams to deliver scalable solutions for critical chip-design workloads.
Share on-call duties, including incident response, root-cause analysis and capacity planning.
What We Need To See:
5+ years of software engineering or infrastructure engineering experience supporting large-scale production systems. You should be able to describe specific incidents you debugged: what broke, and what you changed as a result.
A BS in Computer Science, Engineering, Physics, Mathematics, or a related field, or equivalent experience.
Strong programming experience in Go or Python, including a solid understanding of data structures, algorithms, testing, and software design, with services you have personally built and can walk through in depth. Go is our primary language and preferred, though deep experience in another systems language works too.
Real production experience operating Kubernetes, or a comparable large-scale platform such as an internal developer platform or another cluster manager, with hands-on ownership of specific clusters and components.
Experience building automation that makes decisions and takes action on distributed infrastructure, such as reconciliation loops, controllers, operators, or self-healing remediation systems.
Understanding of performance, security, reliability, fault tolerance, state management, and data consistency in complex systems, built through real infrastructure automation, deployment, and operational recovery work.
A track record of onboarding or migrating other teams onto new infrastructure as a repeatable process: self-service tooling, readiness checks, and documentation that let other teams operate independently afterward.
Strong communication skills and the ability to work optimally across teams, organizations, and geographic regions. You bring a systematic approach to…