Requirements

• Significant experience in software engineering, including large-scale production systems such as ads delivery, ranking, recommendation, search, or marketplaces
• Experience managing engineers with ownership of a broad technical area and accountability for strategy and outcomes
• Experience driving technical strategy and roadmap decisions for large-scale ranking, auction, or ML-driven production systems
• Experience with online experimentation (A/B testing) and data-driven launch decisions balancing competing metrics
• Experience partnering cross-functionally with product, data science, and engineering teams to define problems and deliver measurable outcomes
• Experience recruiting, developing, and retaining engineering talent in high-impact, ambiguous areas

Nice to haves

• Hands-on background in ML model development and production ML systems (e.g., PyTorch), including multi-objective ranking and calibration
• Experience implementing responsible, ethical AI practices such as risk assessment and bias mitigation
• Ability to integrate AI tools to optimize engineering workflows and drive impact in efficiency and quality
• Experience building shared infrastructure/platforms with focus on reliability, developer productivity, and efficiency
• Experience with ad quality, user experience, or integrity signals in ranking and bidding
• Proven track record in building ads delivery, ranking, or auction systems influencing revenue and user experience at scale
• Experience in auction and mechanism design, bid optimization, or marketplace health considering advertiser-side factors
• Experience leading engineers across multiple sites
• Ongoing AI skill development and staying current with emerging AI technologies
• Experience measuring long-term effects of product changes using methods like holdouts or causal inference

What you'll be doing

• Own and lead an ads quality area end to end, managing strategy, roadmap, execution, and outcomes including signal development, modeling, ranking, auction integration, experimentation, and serving
• Define vision and technical strategy for quality-aware ranking, bidding, user value adjustments, and protections against low-quality/harmful ads
• Balance trade-offs between short-term revenue and long-term user and advertiser value through quality measurement and auction pricing
• Engage actively in technical direction, system design, and code quality, keeping up with state of the art in ads ranking, auction design, causal inference, and ML
• Partner with product, data science, engineering, and integrity teams to prioritize experiments and translate improvements into measurable outcomes
• Build and evolve shared ads quality infrastructure and tooling for safe and fast delivery of improvements across the ads organization
• Proactively identify and mitigate execution risks including revenue regressions, quality issues, experiment validity, reliability incidents, and capacity constraints
• Increase experimentation velocity and establish rigorous experimentation and long-term measurement practices
• Recruit, develop, and retain engineers with ads systems expertise and foster strong technical leadership
• Establish a culture of technical excellence, rigorous experimentation, open collaboration, and AI-native working methods
• Maintain accountability for area performance, technical depth, and cross-functional engagement
• Communicate area work and trade-offs clearly to senior leadership