Principal Engineer, YouTube, Visits Quality

YouTube Shorts’ mission is to give everyone a voice and show them the world through short-form videos. For viewers, we aim to provide a high quality, entertaining, and engaging experience. As the "growth engine" of the product, our team is responsible for the AI/ML systems that power Shorts recommendations across the Infinite Playback, Home, WatchNext, Notifications, etc.

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.

• Define technical strategy for enhancing YouTube Shorts discovery models and systems to accelerate viewer and creator growth while improving user satisfaction.
• Provide technical leadership on high-impact projects; design, develop, test, and deploy large-scale recommendation models, novel model architectures, and optimize ML infrastructure to drive the growth of the Shorts ecosystem.
• Partner with engineering, product, data-science, and research teams to convert business goals into scalable technical solutions that grow the Shorts ecosystem.
• Facilitate alignment and clarity across teams on goals, prioritization, outcomes, and timelines. Mentor and influence to uplevel junior engineers on the team.

Minimum qualifications:

• Bachelor's degree in Engineering, Computer Science, or equivalent practical experience.
• 15 years of experience in engineering.
• 8 years of industry experience working on Artificial Intelligence/Machine Learning (AI/ML) recommendations.
• Experience with cross-functional technical innovation.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 15 years of industry experience in the recommendations technology domain.
• Experience with relevant domains such as optimizing user satisfaction and designing multi-task learning models for downstream tasks.