Principal Engineer, YouTube Ads Quality, Marketplace Optimization

Principal Engineer, You Tube Ads Quality, Marketplace Optimization

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By applying to this position you will have an opportunity to share your preferred working location from the following:
San Bruno, CA, USA;
Kirkland, WA, USA;
Mountain View, CA, USA
.

• Master's degree in Computer Science, Machine Learning, or related quantitative field, with a focus on recommender systems, user modeling, or computational advertising, or equivalent practical experience.
• 15 years of experience in software development, including experience in ML/AI.
• Experience with distributed systems and cloud platforms (e.g., Google Cloud Platform).
• Experience in technical roles working with ML algorithms, statistical modeling, and data science principles applied to user-facing products.

• PhD degree in Computer Science, a related technical field, or equivalent practical experience.
• Experience with large-scale data processing technologies for ad relevance models.
• Experience in designing, building, and deploying large-scale production ML systems for ranking, recommendations, or personalization.
• Experience in advertising technology, specifically ad serving, ranking, or optimization, with improvements in user ad experience, coupled with experience leading and delivering impactful ML/AI projects and demonstrating measurable improvements in ad relevance and user satisfaction.
• Proficiency in Python, C++, Java, or Go.

The You Tube Ads Machine Learning/Artificial Intelligence (ML/AI) team is a collaborative group of engineers and researchers pushing advertising technology boundaries. We develop and deploy advanced ML models and systems for the You Tube Ads ecosystem, covering: ad ranking and optimization, building models for relevant and engaging ads, optimizing for clicks, conversions and view-through rates by understanding user intent and context;

targeting and audience segmentation, developing intelligent systems for precise ad targeting and effective audience segmentation, improving user experience through relevance; and new ad product innovation, exploring and implementing novel ML/AI techniques for new, impactful advertising solutions that are less disruptive and more integrated.

As a Principal Engineer for our You Tube Ads ML/AI team, you will be a technical leader, driving the architecture and implementation of critical ML systems for the future of You Tube advertising. Your focus will be on advancing ad relevance to enhance user experience, making ads a valuable part of their journey.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to You Tube creators, with effective advertiser tools that deliver measurable results.

We also enable Google to engage with customers at scale.

The US base salary range for this full-time position is $294,000-$414,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

• Lead technical strategy and roadmap for ML/AI initiatives in You Tube Ads, ensuring scalability, reliability, and performance with an emphasis on ad relevance and user experience.
• Design and develop scalable ML systems and infrastructure for billions of daily requests, prioritizing low-latency relevance predictions, driving selection of ML techniques and tools, and focusing…