Technical Program Manager - AI Labs

NVIDIA•Us, CA

18h•Hybrid

About The Position

NVIDIA is seeking a high-energy, organized Technical Program Manager (TPM) to act as a liaison between AI Labs, internal cross-functional teams, and external partners. This role focuses on building the operational framework to ensure the efficient functioning of the NVIDIA AI Labs team. The TPM will manage the technical engagement rhythm, improve communication, and provide leadership with insights into progress. NVIDIA is at the forefront of Generative AI growth, particularly in large model development, and this position offers a chance to be part of this rapid expansion. The role involves working with NVIDIA's hardware, systems, and software libraries that enable large model builders, and collaborating with leading AI research labs and innovative technologies. Contributions will significantly impact the future of AI model development and Generative AI success, offering substantial professional growth and impact.

Requirements

12+ years in Program/Project Management, ideally within a high-growth tech environment or AI-focused organization.
B.S. degree or equivalent experience.
Familiarity with the AI/ML lifecycle (data labeling, training, inference, and deployment).
Exceptional verbal and written communication skills; ability to explain technical concepts to both technical and non-technical audiences.
Proficiency in project management software and AI tools (Slack, Notion, Miro, etc.).
A "servant leader" mindset focused on enhancing the productivity of others.

Responsibilities

Lead the weekly sync between AI Labs and cross-functional teams (Product, Engineering, Design, QA).
Synthesize feedback, track open issues, and prioritize feature requests to clear technical bottlenecks.
Ensure all internal teams are aligned and progressing in the same direction.
Design and implement the formal process for product team engagement with AI Labs, from initial request to model handoff.
Build the operational system for cross-functional collaboration, including standardized templates, documentation hubs, and communication channels.
Implement and maintain robust tracking systems (e.g., Jira, Linear, or Asana) for real-time updates on team velocity and health.
Oversee the transition of AI features from the lab environment into the production codebase, ensuring rigorous testing and deployment standards.
Translate complex technical updates into concise status reports and dashboards for leadership.
Proactively identify risks in the development cycle and present data-driven solutions to senior collaborators.