NVIDIA is redefining what is possible with AI, and we are building the next generation of relational deep learning for enterprise data. Our platform learns directly from the structure and relationships inside relational databases and heterogeneous graphs, unifying GPU‑accelerated graph analytics and graph machine learning in a single stack.
As Principal Technical Program Manager, you will lead the relational deep learning program from research to production. You will connect ML researchers, infrastructure, and platform teams so work stays aligned and delivers real impact.
Responsibilities:
- Deliver task‑specific models for domains such as fraud detection and recommender systems, moving from problem definition and data requirements through training, benchmarking, and hand‑off to product and customer teams.
- Coordinate closely with infrastructure, systems, and platform groups to align compute capacity, training and serving environments, and platform features that models depend on.
- Guide release management for both the platform and models, including experiment‑to‑production hand‑offs, versioning, compatibility, model cards, benchmarks, and safety and compliance approvals.
- Maintain the operating rhythm for the program, leading planning, reviews, risk and dependency tracking, and decision forums across research, engineering, data, product, legal, and other partners.
- Define and track program health metrics such as model quality, training speed, evaluation coverage, and time‑to‑release, and share clear status, risks, and decisions in executive reviews.
Requirements:
- Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent experience.
- 15+ years of experience in technical program management, engineering, or data/ML delivery, including significant time in ML/AI or large‑scale data environments.
- Experience leading complex, multi‑stakeholder programs end to end in research and engineering organizations, with evolving requirements and clear delivery timelines.
- Comfort working with ML researchers, interpreting model and evaluation results, and making decisions about training pipelines, data, and infrastructure trade‑offs.
Benefits:
- Comprehensive benefits: medical, dental, and vision insurance; a 401(k) with company match; an employee stock purchase plan; flexible, generous paid time off; parental leave; and ongoing learning and development support!
- Equity and benefits eligibility
