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About the Role
Meta is seeking an Engineering Manager to lead an area within Core Ads Quality, responsible for building systems that determine ad quality across Facebook, Instagram, and Messenger. This role involves owning an ads delivery stack area, from quality signals and prediction models to ranking, auction mechanisms, experimentation, and measurement. You will define the vision and technical strategy for ad quality modeling, pricing, and protection in the auction, balancing revenue with long-term value. Collaboration with product, data science, data engineering, and integrity teams is key to delivering impactful changes.
Responsibilities
- Own an ads quality area end to end, accountable for its strategy, roadmap, execution, and outcomes across signal development, modeling, ranking and auction integration, experimentation, and production serving
- Define the vision and technical strategy for the area, including quality-aware ranking and bidding, user value adjustments in the auction, and quality protections against low-quality and harmful ad experiences
- Own the trade-off between short-term revenue and long-term user and advertiser value: define how quality is measured, how it is priced into the auction, and how launch decisions account for long-term effects
- Actively engage with technical direction, system design, and code quality, staying current with the state of the art in ads ranking, auction design, causal inference, and ML
- Partner with product, data science, data engineering, and integrity to define problem formulations, prioritize experiments, and translate system improvements into measurable business and user outcomes
- Build and evolve shared ads quality infrastructure — signal libraries, bid aggregation, experimentation and reporting tooling — so that engineers across the ads organization can ship quality improvements safely and quickly
- Proactively identify and resolve execution risks, including revenue regressions, signal and label quality issues, experiment validity problems, reliability incidents, and capacity constraints
- Increase experimentation velocity and reduce time to decision, establishing rigorous experimentation and long-term measurement practices
- Recruit, develop, and retain engineers with deep expertise in ads systems, and grow strong technical leaders within the area
- Establish a culture that values technical excellence, rigorous experimentation, open collaboration, and AI-native ways of working
- Hold the area accountable for performance, technical depth, and cross-functional engagement
- Represent the area's work to senior leadership, communicating revenue and quality trade-offs, strategic bets, and their implications clearly
Requirements
- Significant experience in software engineering, including work on large-scale production systems such as ads delivery, ranking, recommendation, search, or marketplaces
- Experience managing engineers, including ownership of a broad technical area with accountability for its 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 using data to make launch decisions that balance competing metrics
- Experience partnering cross-functionally with product, data science, and other engineering groups to define problem scope and deliver measurable outcomes
- Experience recruiting, developing, and retaining engineering talent in ambiguous, high-impact areas
Skills
- ML model development
- Production ML systems (e.g., PyTorch)
- Multi-objective ranking
- Calibration
- Responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Integrating AI tools to optimize or redesign engineering workflows
- Building shared infrastructure and platforms
- Ad quality
- User experience
- Integrity signals in ranking (e.g., quality bids, negative feedback, post-click and post-purchase quality, low-quality and harmful content)
- Ads delivery
- Ranking
- Auction systems
- Auction and mechanism design
- Bid optimization
- Marketplace health
- Advertiser-side considerations
- Prompt/context engineering
- Agent orchestration
- Measuring long-term effects of product changes (e.g., holdouts, long-term causal inference, user learning effects)
About the Company
- Meta is seeking an Engineering Manager to own and lead an area within Core Ads Quality, building the systems that determine the quality of ads people see across Facebook, Instagram, and Messenger.