Senior Software Engineer, Platform Engineering - EDA Infrastructure

NVIDIA Corporation; California; On-site; USD 152,000 - 288,000; Full time

Job summary

NVIDIA Corporation is seeking engineers to design and operate a governed cloud-scale infrastructure platform powering our EDA and hardware teams. You will manage Kubernetes clusters, build self-service tools, and extend the platform with controllers and operators to enable scalable production workflows.

The role emphasizes strong Go/Python programming, distributed systems experience, and on-call responsibilities across multiple sites, including onboarding teams and ensuring security and

Qualifications

• 5+ years of software or infrastructure engineering experience supporting large-scale production systems.
• BS in Computer Science, Engineering, Physics, Mathematics, or related field, or equivalent experience.
• Strong programming in Go or Python, with solid data structures, algorithms, testing, and design.

Responsibilities

• Own the lifecycle of the Kubernetes clusters/platform, including control plane, node lifecycle, upgrades, provisioning, storage, and networking.
• Build self-service capabilities for compute, secrets, certificates, DNS, access, deployment.
• Extend Kubernetes with controllers and operators, using open-source or in-house solutions.

Skills

Distributed systems

Go

Python

Incidents

On-call

Communication

Education

BS in Computer Science or related field

Tools

Kubernetes

Cluster API

Ceph

Cilium

Argo CD

Job description

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 problem solving, a strong sense of ownership, and a habit of eliminating operational toil.

Ways To Stand Out From The Crowd

• Contributions to open‑source infrastructure projects, such as bug fixes or features you sent upstream instead of working around them.
• Experience running Kubernetes on bare metal with tools like Cluster API, Metal3, Ceph, or Cilium. Exposure to EDA applications, license‑management systems, high‑throughput batch workloads, or semiconductor design workflows is a plus.
• Hands‑on experience with writing your own operators or the cloud‑native ecosystem, such as cert‑manager, external‑dns, Argo CD, or Kyverno.
• A track record of being the engineer other teams' onboarding or migration efforts route through. You build the paved road other people's migrations travel on.
• You've also improved infrastructure reliability, utilization, and recovery time through production‑quality automation, operating infrastructure across multiple data centers or heterogeneous hardware environments, on‑premises and in the cloud.

NVIDIA is widely considered one of the technology world’s most desirable employers. We have some of the most forward‑thinking and hardworking people in the industry working with us. If you are creative, autonomous, and excited to build reliable infrastructure at scale, we want to hear from you!

Join a team of engineers at NVIDIA dedicated to scaling the AI infrastructure that powers cutting‑edge data science applications. Our team solves complex challenges across datacenter hardware, networking, distributed systems, and software deployment. We collaborate closely to design and deliver leading infrastructure solutions. We value strong technical foundations paired with creative thinking and a bias toward execution. Together, we continuously push boundaries, grow our skills, and advance NVIDIA’s capacity to build and deploy AI‑based infrastructure at scale.

Your base salary will be determined based on your location, experience, and other objective job‑related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 14, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.