Overview
Job title: Program, Project Manager - Technical Program Manager
Work on NVIDIA Metropolis platform offering GPU-accelerated libraries, SDKs, microservices, tools, and blueprints.
Enable development, deployment, and scaling of AI-enabled applications from edge to cloud. Lead full lifecycle concept, model training, optimized inference, and deployment.
Job Details
- Work Location (Tax): Santa Clara, California, USA
- Start Date: June 1, 2026
- End Date: November 30, 2026
Key Responsibilities
- Work closely with engineering and product management to drive strategic roadmap execution.
- Collaborate in Agile ceremonies sprint planning, stand-ups, reviews, and retrospectives.
- Use Jira dashboards and bug databases for reporting; Confluence for program updates.
- Track KPIs with engineering teams to improve execution and deliverables.
- Act as integral member of Vision AI development and productization workflows.
- Facilitate communication between stakeholders and leadership to align goals and resolve dependencies.
Required Skills & Qualifications
- Education: Bachelor`s degree in Electrical Engineering or Computer Science or equivalent experience.
- Experience: 6+ years of end-to-end Program Management experience; experience in similar or related roles.
- Core Skills: Excellent verbal, written, interpersonal, and presentation skills; Proficiency in Agile methodologies; Strong experience with Atlassian tools (Jira, Confluence) and ability to guide teams; Highly organized, detail-oriented, strong listening skills; Ability to work in fast-paced, high-uncertainty environments and adapt quickly; Strong prioritization and alignment-building skills; Ability to think long-term and build consensus.
Ways to Stand Out
- Experience in Vision AI or AI application development lifecycle (data acquisition, labeling, training, optimization, deployment).
- Understanding of Embedded Systems (NVIDIA GPU familiarity is a plus).
- Deep understanding of Software Product Lifecycle for production-grade AI.
- Use of AI in Program Management to improve workflows and team velocity.
- Experience managing AI-specific challenges like model accuracy KPIs and human-in-the-loop validation.