Principal Technical Program Manager, Relational Deep Learning Platform

About the role

NVIDIA is seeking a Principal Technical Program Manager to lead the development and deployment of advanced AI solutions, specifically focusing on relational deep learning for enterprise data. This role requires a deep understanding of ML/AI principles, strong leadership skills, and the ability to manage complex, multi-stakeholder programs.

Responsibilities

• Lead the relational deep learning program from research to production, ensuring alignment across ML researchers, infrastructure, and platform teams.
• Create and deliver task-specific models for domains like fraud detection and recommender systems, managing the entire lifecycle from problem definition to model hand-off to product and customer teams.
• Coordinate with infrastructure, systems, and platform groups to ensure compute capacity, training, and serving environments meet model needs.
• Manage release processes for both the platform and models, including experiment-to-production hand-offs, versioning, compatibility, model cards, benchmarks, and safety and compliance approvals.
• Define and track program health metrics, such as model quality, training speed, evaluation coverage, and time-to-release, and present clear status updates in executive reviews.
• Build and maintain operating rhythms for the program, leading planning, reviews, risk and dependency tracking, and decision forums across various teams.

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.
• Ability to build operating rhythms from scratch, influence without formal authority, and communicate clearly with both highly technical teams and senior executives.
• Familiarity with modern program-management practices (such as Agile, roadmapping, risk and dependency management) and the judgment to use them effectively in fast-moving research settings.
• A hands-on, builder mindset that includes creating automation and tooling, using AI in daily work, and applying AI to streamline program operations, status reporting, risk detection, and release workflows.

Qualifications

• Experience delivering graph ML, recommender, or fraud-detection systems into production.
• Working knowledge of graph machine learning, GNNs, relational or tabular data, and graph analytics libraries such as cuGraph.
• Experience running programs that span platform and infrastructure teams and model and research teams, including GPU and compute capacity planning for training and serving.

Skills

• Strong leadership and project management skills.
• Experience with ML/AI research and development.
• Ability to interpret and communicate complex model and evaluation results.
• Knowledge of modern program-management practices and their effective use in fast-moving research settings.
• Hands-on experience in creating automation and tooling, using AI in daily work, and applying AI to streamline program operations.

Benefits

• Comprehensive benefits package including 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!
• Base salary range: $240,000 - $379,500, depending on location, experience, and the pay of employees in similar positions.
• Eligibility for equity and benefits.

Pay

$240,000 - $379,500 per year, depending on location, experience, and the pay of employees in similar positions.

Schedule

N/A

Benefits

• Medical, dental, and vision insurance
• 401(k) with company match
• Employee stock purchase plan
• Flexible, generous paid time off
• Parental leave
• Ongoing learning and development support

Application Instructions

Applications for this job will be accepted at least until August 31, 2026.

Ways to Stand Out

Delivering graph ML, recommender, or fraud-detection systems into production, or shipping ML platforms and frameworks that other teams build on.

Working with graph machine learning, GNNs, relational or tabular data, graph analytics libraries such as cuGraph, and the modern data stack (warehouses, feature stores, and data pipelines).

Running programs that span platform and infrastructure teams and model and research teams, including GPU and compute capacity planning for training and serving.