Technical Program Manager - AI

NVIDIA•Us, CA

About The Position

An AI Program Manager is a strategic leader responsible for overseeing a portfolio of AI/ML programs to achieve the goals of the end‑user community, bridging the gap between technical teams (data scientists, AI engineers) and end users to ensure initiatives align with business strategy, are delivered on time and within budget, and adhere to ethical standards. They lead cross-functional teams and manage dependencies across multiple AI projects. They prioritize resources and ensure AI models are developed and deployed effectively to deliver business value. Their work is a blend of technical fluency, project management discipline, and cross-functional leadership skills.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field or equivalent experience (Master’s degree or MBA is often preferred).
12+ years in program management, with significant experience in AI/ML, data science, or software engineering.
Strong understanding of AI/ML concepts, and direct experience managing AI programs.
Proven record of leading complex, global programs through full development lifecycles into deployment and production.
Experience managing complex programs that require leading the engagement between the development and end user community.
Proven ability to coordinate with global teams and navigating multi-cultural, multinational business environments.
Excellent interpersonal skills with the ability to distill complex technical concepts for diverse technical and non-technical audiences, from engineers to executives.
Excellent ability to translate sophisticated technical concepts into actionable business insights for non-technical collaborators. This includes experience developing technical documentation.

Nice To Haves

Strong background thriving in dynamic, high-growth environments, with proven adaptability to ambiguity, rapid change, and cross-functional, matrixed team structures.
Strong leadership in cross-functional collaboration, conflict resolution, and consensus-building.
Proven track record to operationalize Responsible AI practices, embedding governance, model risk management, and ethical review into the AI lifecycle.

Responsibilities

Define and continuously refine the enterprise AI strategy, roadmap, and achievements to steer a cohesive portfolio of AI initiatives that materially advance business and customer outcomes.
Translate complex, often ambiguous business needs from the user community into precise, scalable technical requirements and architectural direction that enable high‑velocity execution by development teams.
Orchestrate cross-functional collaboration across data scientists, ML engineers, product managers, and senior business collaborators to remove execution barriers and ensure end-to-end, high-quality delivery.
Champion a user‑anchored approach by ensuring those closest to the problem—the user community—shape problem definitions, success metrics, and validation criteria for all AI solutions.
Provide technical leadership across the AI lifecycle, from data strategy and model development through evaluation, deployment, and continuous optimization, ensuring solutions are robust, reliable, and production‑ready.
Establish and govern program‑level performance metrics (e.g., model accuracy, latency, time‑to‑market, return on investment), synthesizing insights into executive‑caliber reporting and recommendations that influence strategic decisions.
Lead organizational change to embed AI‑enabled ways of working, including upskilling and enabling the user community to confidently adopt, test, and iterate on AI products.
Operate as a force multiplier by combining deep technical fluency with senior‑level program leadership to drive alignment, secure sponsorship, and unlock step-change impact from the AI portfolio.