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# Technical Program Manager, AI Inference

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, United States (Remote available)
- **Experience:** Expert
- **Salary:** $168,000.0 - $258,750.0
- **Contract:** Permanent contract
- **Skills:** Board Bringup, Agile Methodology, Artificial Intelligence, Machine Learning, Open Source Technology, Software Engineering, AI Infrastructure, Graphics Processing Unit (GPU), Deep Learning, Information Technology
- **Published:** October 23, 2026
- **Apply:** https://startup.jobs/senior-technical-program-manager-ai-inference-2100-nvidia-usa-10572113

## About the Role

* BS degree in Computer Science, Artificial Intelligence, or equivalent experience plus 10 years of experience leading global technical programs.
 * Solid knowledge of software engineering standard methodologies, hardware bring-up processes, and enterprise quality standards.
 * Proficiency with Scaled Agile frameworks and program tracking tools.
 * Demonstrated ability to resolve resource conflicts, unblock teams, and drive alignment at every level of the organization.
 * Clear, compelling communication skills across both technical and business audiences.

Ways to Stand Out from the crowd:

 * Proven experience in machine learning, deep learning, GPU technology, or AI infrastructure.
 * Experience shipping production AI software or co-managing software-hardware programs end to end.
 * A record of building scalable program management practices in growing engineering organizations.
 * Mentorship instincts - the kind of person who invests in the teams around them.
 * Familiarity with open-source inference frameworks or model optimization tooling.

## Description

* Drive planning, scheduling, dependency tracking, and executive reporting across multiple concurrent inference programs.
 * Partner with hardware and software engineering leads to align library development with new and upcoming hardware timelines.
 * Coach teams on agile practices and run the rituals that keep programs on track.
 * Build and maintain metrics that surface risks early and support decisions with real data.
 * Communicate program status and technical context clearly to engineering teams and senior leadership alike.
 * Align global, cross-disciplinary teams spanning hardware, software, and product.
 * Identify process gaps and implement improvements that increase delivery quality and team efficiency.
 * Moderate complex technical discussions and drive them to clear, documented conclusions.