Senior Software Engineer, SRE and Production Engineering - DGX Cloud

AI in this role

Build and operate large-scale GPU infrastructure and cloud automation for AI workloads at NVIDIA DGX Cloud.

gopythonkuberneteslinuxbmcredfishbluefield-dpusnvidia-gpus

sreproduction-engineeringinfrastructure-automationhardware-validationcluster-management

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:

5+ 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!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 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 October 3, 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.

How we rate this

Senior Software Engineer, SRE and Production Engineering - DGX Cloud at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

SreProduction EngineeringInfrastructure AutomationHardware ValidationCluster ManagementGoPythonKubernetes

Questions you could be asked

Tell me about a project where sre was part of your work. What did you do?
Tell me about a project where production engineering was part of your work. What did you do?
Tell me about a project where infrastructure automation was part of your work. What did you do?
Tell me about a project where hardware validation was part of your work. What did you do?
Tell me about a project where cluster management was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Sre, Production Engineering, Infrastructure Automation, Hardware Validation, and Cluster Management. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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