Software Engineering Manager, Ads Systems

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. In this role, you will be accountable for an area of Meta's ads delivery stack — from quality signals and prediction models to ranking and auction mechanisms, large-scale experimentation, and long-term measurement. You will set the vision and technical strategy for how ad quality is modeled, priced, and protected in the auction, balancing short-term revenue with long-term value for people and businesses. You will partner closely with product, data science, data engineering, and integrity to deliver changes that have measurable impact on billions of people and millions of advertisers.

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

Qualifications

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 Hands-on background in ML model development and production ML systems (e.g., PyTorch), including multi-objective ranking and calibration
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ability to integrate AI tools to optimize or redesign engineering workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience building shared infrastructure and platforms used across an organization, with a focus on reliability, developer productivity, and efficiency
Experience with ad quality, user experience, or integrity signals in ranking (e.g., quality bids, negative feedback, post-click and post-purchase quality, low-quality and harmful content)
Track record of building ads delivery, ranking, or auction systems that directly influenced revenue and user experience metrics at significant scale
Experience with auction and mechanism design, bid optimization, or marketplace health, including advertiser-side considerations
Experience leading engineers across multiple sites
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience measuring long-term effects of product changes (e.g., holdouts, long-term causal inference, user learning effects)