NVIDIA researchers depend on GPU clusters for large-scale AI workloads. Our DGX Cloud Kubernetes Runtime & Release team brings those clusters to life across major public clouds and specialized GPU providers, often on hardware that is new to the world when we get it. We build and maintain the supported Kubernetes runtime, automate its delivery, and bring new providers and GPU platforms into production.
We’re growing quickly and taking on broader ownership of NVIDIA’s cluster software delivery. We’re hiring across Runtime, Release Engineering, and Provider Integration, with each role focused on your strengths. You don’t need experience across every area below.
What you’ll be doing:
Your primary focus will be one of three areas, with collaboration across the team:
- Runtime: Build Go controllers and APIs to install, upgrade, and validate GPU cluster software. Integrate components, define API contracts, and evolve Helm and Argo CD delivery toward controller-driven automation.
- Release Engineering: Build validation pipelines that inform release decisions across providers and GPU platforms. Develop systems to allocate GPU capacity across validation runs and account for cloud reservations and quotas. Make qualification more efficient through reusable tests and clear failure reports.
- Provider Integration: Bring new providers and GPU hardware into production, potentially among the first engineers working with new silicon. Resolve integration failures with partner teams and turn initial provisioning, upgrade, and operational checks into repeatable automation.
What we need to see:
- 6+ years building production infrastructure software or distributed systems.
- Strong programming skills in Go or another language to build production systems, with willingness to work primarily in Go.
- Kubernetes experience and depth in at least one area: controllers and operators, release automation, test and validation systems, or cloud integration.
- Experience delivering engineering projects, diagnosing complex failures, and collaborating across teams.
- BS or MS in Computer Science, Engineering, or equivalent experience.
Ways to stand out from the crowd:
- Experience in any of these areas is valuable, but not required:
- Go development with controller-runtime, CRDs, and reconcilers.
- Release qualification across multiple environments or platforms.
- GPU infrastructure, accelerated networking, or GPU scheduling.
- Bringing new hardware, regions, or cloud providers into production.
- Resource allocation, leasing, or fair-share scheduling and upstream integration, compatibility, or software supply chain integrity.
This role suits an engineer who wants direct influence over what reaches production, and who builds for the hundredth cluster while shipping the first. Join us and help build the next generation of NVIDIA’s GPU cloud infrastructure!
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:184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
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.Skills Required
- 6+ years building production infrastructure software or distributed systems
- Strong programming skills in Go or another language, with willingness to work primarily in Go
- Kubernetes experience with depth in controllers and operators, release automation, test and validation systems, or cloud integration
- Experience delivering engineering projects, diagnosing complex failures, and collaborating across teams
- BS or MS in Computer Science, Engineering, or equivalent experience
- Go development with controller-runtime, CRDs, and reconcilers
- Release qualification across multiple environments or platforms
- GPU infrastructure, accelerated networking, or GPU scheduling experience
- Experience bringing new hardware, regions, or cloud providers into production
- Resource allocation, leasing, or fair-share scheduling experience
- Upstream integration, compatibility, or software supply chain integrity experience
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
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Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
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Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
