Senior Software Engineer, Platform Engineering - EDA Infrastructure

NVIDIA•Durham, NC

3d•$152,000 - $287,500

About The Position

NVIDIA is hiring a Senior Software Engineer to build and operate the governed infrastructure platform supporting its Electronic Design Automation (EDA) and hardware engineering organization. The platform provides teams with a self-service path from source code to production across cloud environments and physical sites. The role requires strong programming skills, distributed-systems knowledge, and experience operating large-scale production infrastructure. The engineer will build reusable platform capabilities, favor suitable open-source solutions, and improve developer experience, reliability, and operational efficiency as the fleet and number of onboarded teams grow. The role spans the cloud-native stack, from Kubernetes to cluster schedulers, and includes responsibility for Kubernetes clusters, bare-metal provisioning, storage, networking, CI/CD, and shared platform services. The engineer will develop self-service tooling and Kubernetes extensions, onboard teams and sites through repeatable processes, and collaborate with EDA, infrastructure, networking, storage, and hardware engineering teams. On-call responsibilities include incident response, root-cause analysis, and capacity planning.

Requirements

At least 5 years of software engineering or infrastructure engineering experience supporting large-scale production systems, with specific experience debugging incidents and explaining resulting changes.
BS in Computer Science, Engineering, Physics, Mathematics, or a related field, or equivalent experience.
Strong programming experience in Go or Python, with understanding of data structures, algorithms, testing, and software design, and services personally built and explainable in depth. Go is preferred; deep experience in another systems language is also acceptable.
Production experience operating Kubernetes or a comparable large-scale platform, with hands-on ownership of specific clusters and components.
Experience building automation that makes decisions and acts 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, gained through infrastructure automation, deployment, and operational recovery work.
Experience onboarding or migrating teams onto new infrastructure as a repeatable process, including self-service tooling, readiness checks, and documentation that enables independent operation.
Strong communication and cross-team collaboration skills, systematic problem-solving, ownership, and a habit of eliminating operational toil.

Nice To Haves

Contributions to open-source infrastructure projects, such as upstream bug fixes or features.
Experience running Kubernetes on bare metal with tools such as Cluster API, Metal3, Ceph, or Cilium.
Exposure to EDA applications, license-management systems, high-throughput batch workloads, or semiconductor design workflows.
Hands-on experience writing operators or working with cloud-native projects such as cert-manager, external-dns, Argo CD, or Kyverno.
Experience serving as a key engineer for other teams’ onboarding or migration efforts and building reusable migration paths.
Experience improving infrastructure reliability, utilization, and recovery time through production-quality automation.
Experience operating infrastructure across multiple data centers or heterogeneous hardware environments, on-premises and in the cloud.

Responsibilities

Own the lifecycle of Kubernetes clusters, including the control plane, node lifecycle, and fleet-wide upgrades.
Own the bare-metal provisioning, storage, and networking underlying the clusters.
Build self-service capabilities for compute, secrets, certificates, DNS, access, and deployment.
Extend Kubernetes with controllers and operators, using open-source projects where appropriate and developing custom solutions when needed.
Own CI/CD pipelines and shared platform services, including policy, autoscaling, and observability, to provide teams with a secure path to production by default.
Onboard teams and sites using a repeatable process; define onboarding requirements and build self-service tooling and documentation that enable independent operation.
Work with EDA, infrastructure, networking, storage, and hardware engineering teams to deliver scalable solutions for chip-design workloads.
Share on-call duties, including incident response, root-cause analysis, and capacity planning.