NVIDIAHiring

Senior Technical Program Manager, OEM Enablement

  • Remote, United States
  • Lead
  • Posted yesterday
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In one line

Program management role on a solutions architecture team, coordinating with hardware partners so AI data centers get built, deployed and supported well.

About the role

Owns programs that close readiness gaps across partner engineering, supply chain and support, from training and debugging to spares and service readiness.

At a glance

Pay
Not stated in the posting
Remote scope
Remote, United States
Seniority
Lead
Skills
Data centers, Hardware systems, AI infrastructure, Debugging
Posted
28 Oct 2026
Last checked
28 Oct 2026

What you will do

  • Lead technical programs across AI data center development, deployment and support with engineering and hardware partners.
  • Assess partner service and support against customer needs and build programs to close gaps in training, debugging, spares and service readin
  • Align solutions architects, field application engineers, sales, account management and engineering on requirements and expectations.
  • Use field data and feedback to improve partner workflows and turn lessons into repeatable fixes.

What they look for

  • Degree in electrical engineering, computer science, IT or a related technical field, or equivalent experience.
  • Twelve or more years in enterprise or hyperscale data centers, hardware systems or similar infrastructure, including leading cross-functiona
  • Technical depth to work with engineering on AI systems, product architecture and system dependencies.

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Message for Muse
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Muse is available in the US only for now.

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Questions about this job

Can I do this job remotely, and from which countries?

The posting lists it as remote for people in the United States.

What does this job pay?

The posting does not state a salary, so there is no figure to quote.

What experience is needed?

It is a lead role. The posting asks for: degree in electrical engineering, computer science, IT or a related technical field, or equivalent experience; twelve or more years in enterprise or hyperscale data centers, hardware systems or similar infrastructure, including leading cross-functiona; technical depth to work with engineering on AI systems, product architecture and system dependencies. Skills it names include Data centers, Hardware systems, AI infrastructure, Debugging.

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