Senior Customer Technical Program Manager - AI Datacenter
$168,000–$258,750 year
RemoteUnited States or Santa Clara, California, United States
Job Summary
Define program schedules, deliverables, and key milestones aligned with customer and partner requirements for AI infrastructure deployments. Translate customer needs into actionable plans across engineering, quality, logistics, and sales while driving issues to resolution before they become roadblocks. Lead horizontally across cross-functional teams to navigate critical issues, serve as the face of NVIDIA during kick-offs and design reviews, and champion post-deployment sustaining support. Monitor factory production schedules, yields, and blockers in partnership with ODMs and contract manufacturers to hit operational targets. Communicate program health, risks, and key issues proactively to customers and leadership across Hardware, Software, Operations, and Product teams.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience
- 8+ years of program or project management experience in the IT industry
- Strong technical background in hardware and/or software engineering
- Experience working closely with hyperscalers and their OEM/ODMs on AI factory build-outs
- A proven track record of leading complex, global programs from concept through deployment and production
- Outstanding program management, communication, and organizational skills
- Demonstrated ability to coordinate across global teams and navigate multi-cultural, multinational business environments with ease
- Excellent interpersonal skills
- Willingness to travel as required (up to 5%)
Desired Qualifications
- Track record running end-to-end hardware and software product development and datacenter deployment
- Prior experience with deploying accelerated computing platform at-scale
- Strong leadership in matrixed collaboration, conflict resolution, and consensus-building
- Proven expertise managing production schedules, optimizing line yields, and resolving complex field quality challenges
- Demonstrated success in dynamic, high-growth environments
- Proven adaptability to ambiguity, rapid change, and matrixed team structures
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