About the job
NVIDIA Metropolis offers GPU-accelerated libs/SDKs, Microservices, Tools and Blueprints that help developers build, deploy and scale AI-enabled applications – from the edge to the cloud. This suite of software offerings provides a variety of starting points to accelerate and optimize any aspect of AI application development and deployment. We are looking for a passionate Technical Program Manager to be part of this journey. This role will be leading all steps of the development lifecycle: from concept to model training, engineering for optimized inference and deployment.
Responsibilities
Closely work with engineering and product management to deliver and drive strategic roadmap execution
Collaborate with engineering on sprint planning, stand ups, sprint review and retrospective
Use Jira dashboard and bug database queries to help with various reporting, and Confluence to provide comprehensive program updates to the core and leadership teams
Work closely with the engineering on various KPIs to improve overall execution process and deliverable
Integral member of Vision AI development and productization workflow
Qualifications
Minimum
Bachelors in Electrical Engineering or Computer Science or equivalent experience
8+ years of end-to-end Program Management experience in a similar or related role
Excellent verbal, written, interpersonal and presentation skills
Proficient in Agile project management methodologies
Experience with Jira to the point that you can comfortably guide an engineering team on how to use these tools and implement in an agile/scrum manner
Proven track record of being well organized, detail oriented, have excellent listening skills, in an environment with shifting priorities and changing requirements
Ability to think long-term and build consensus to make programs successful
Outstanding skills in prioritizing and building alignment
Preferred
Prior Program Management experience with Vision AI or AI application development including Data Acquisition and Labeling to Model Training, Optimization, and Edge Deployment
Strong understanding of Embedded Systems (familiarity with Nvidia GPUs is a plus)
Deep understanding of Software Product Life Cycle to develop production grade AI applications
AI adoption in Program Management to optimize workflows and increase team velocity
Experience managing the unique challenges of AI programs, such as, Model accuracy KPIs, and "human-in-the-loop" validation processes