Principal Engineer, YouTube Ads Quality

Minimum qualifications:

• 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).
• Technical experience working with ML algorithms, statistical modeling, and data science principles applied to user-facing products.
  

Preferred qualifications:

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

About the job

Our YouTube Ads 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 YouTube Ads ecosystem, covering: Ad Ranking & Optimization, building models for relevant and engaging ads, optimizing for clicks, conversions and view-through rates by understanding user intent and context; Targeting & 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.

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 YouTube 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.

Responsibilities

• Lead technical strategy and road map for ML/AI initiatives in YouTube 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, and driving selection of ML techniques and tools, focusing on models that optimize for advertiser return on investment (ROI) and positive user sentiment.
  
• Mentor engineers, fostering excellence in building user-centric ad experiences and collaborating with product managers and researchers to define requirements and deliver high-impact features improving ad relevance and user satisfaction.
  
• Identify and mitigate technical risks, and balance short-term needs with long-term goals for sustainable ad relevance.
  
• Stay current with ML/AI and advertising tech, identifying opportunities to enhance ad performance and user perception.