NVIDIA DGX Cloud builds and operates large-scale GPU infrastructure for AI workloads. We are looking for Software Engineers with SRE or Production Engineering experience who have worked hands-on with bare-metal NVIDIA systems. This team builds the software and operational tooling that moves GPU capacity from installed hardware to production service supporting an IaaS production environment of BMaaS, VMaaS.
What you’ll be doing:
- Build automation for bare-metal provisioning, hardware validation, firmware and software upgrades, repair, and cluster lifecycle management.
- Develop tools that interact with BMC and Redfish interfaces to monitor hardware health, manage server state, and assist recovery workflows.
- Handle and advance NVIDIA NVL72 systems and BlueField-3 or later DPUs throughout cloud partner and on-premises environments.
- Diagnose failures across servers, DPUs, GPU systems, CPU systems, networking, Linux, and Kubernetes; turn recurring issues into automated detection and repair.
- Define validation and handoff criteria so new capacity enters production safely and consistently.
- Take part in on-call duties, incident response, root-cause analysis, and follow-up to implement permanent solutions.
- Work with hardware, networking, platform, data center operations, and partner teams to resolve issues across ownership boundaries.
What we need to see:
- 12+ years building software for or operating production infrastructure, including substantial hands-on bare-metal experience.
- Strong Go or Python skills, with a record of delivering production automation and services.
- Direct experience with BMC and Redfish in server provisioning, health inspection, power management, or fault diagnosis.
- Practical experience working directly with NVIDIA GPU hardware, such as NVL72 systems, and BlueField-3 or newer DPUs.
- Experience with Linux, firmware and driver management, network boot, and the server lifecycle from initial provisioning through repair.
- Experience managing production reliability through on-call duties, incident response, observability, and durable solutions.
- Ability to debug failures across hardware, host operating systems, networking, and distributed services.
- Clear communication and demonstrated ownership of problems that span multiple teams.
- BS/MS in Computer Science or equivalent experience in a practical setting.
Ways to stand out from the crowd:
- Experience operating BlueField DPUs in DPU mode, including host-to-DPU connectivity and lifecycle debugging, with DPU or equivalent experience considered.
- Background with NVLink, InfiniBand, Spectrum-X, or GPU cluster performance validation.
- Experience building safe, repeatable workflows for rack-scale bringup, firmware upgrades, hardware replacement, and customer handoff.
- Experience with Kubernetes, GitOps, Argo CD, SLOs, and fleet-wide automation.
At NVIDIA, you’ll help make next-generation AI infrastructure production-ready at scale!
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
Skills Required
- 12+ years building software for or operating production infrastructure, including substantial hands-on bare-metal experience
- Strong Go or Python skills with experience delivering production automation and services
- Direct experience with BMC and Redfish for server provisioning, health inspection, power management, or fault diagnosis
- Hands-on experience with NVIDIA GPU hardware, such as NVL72 systems, and BlueField-3 or newer DPUs
- Experience with Linux, firmware and driver management, network boot, and the server lifecycle from provisioning through repair
- Experience with on-call duties, incident response, observability, and implementing durable reliability solutions
- Ability to debug failures across hardware, host operating systems, networking, and distributed services
- Clear communication and ownership of problems spanning multiple teams
- BS or MS in Computer Science, or equivalent practical experience
- Experience operating BlueField DPUs in DPU mode, including host-to-DPU connectivity and lifecycle debugging
- Experience with NVLink, InfiniBand, Spectrum-X, or GPU cluster performance validation
- Experience building workflows for rack-scale bringup, firmware upgrades, hardware replacement, and customer handoff
- Experience with Kubernetes, GitOps, Argo CD, SLOs, and fleet-wide automation
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 — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
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.”
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