Location
London

Hours
Full Time

Salary
Negotiable

About the Role
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 teams to deliver changes that have measurable impact on billions of people and millions of advertisers.

Key responsibilities include:
- Owning 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
- Defining 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
- Balancing short-term revenue and long-term user and advertiser value by defining how quality is measured, priced into the auction, and how launch decisions account for long-term effects
- Engaging actively with technical direction, system design, and code quality, staying current with state-of-the-art ads ranking, auction design, causal inference, and machine learning
- Partnering cross-functionally to define problem formulations, prioritize experiments, and translate system improvements into measurable business and user outcomes
- Building and evolving shared ads quality infrastructure such as signal libraries, bid aggregation, experimentation, and reporting tooling to enable safe and rapid quality improvements
- Proactively identifying and resolving execution risks including revenue regressions, signal and label quality issues, experiment validity problems, reliability incidents, and capacity constraints
- Increasing experimentation velocity and reducing time to decision by establishing rigorous experimentation and long-term measurement practices
- Recruiting, developing, and retaining engineers with deep expertise in ads systems and growing strong technical leaders within the area
- Establishing a culture that values technical excellence, rigorous experimentation, open collaboration, and AI-native ways of working
- Holding the area accountable for performance, technical depth, and cross-functional engagement
- Representing the area's work to senior leadership, clearly communicating revenue and quality trade-offs, strategic bets, and their implications

Experience
- Significant experience in software engineering, including work on large-scale production systems such as ads delivery, ranking, recommendation, search, or marketplaces
- Experience managing engineers and owning a broad technical area with accountability for strategy and outcomes
- Proven ability to drive technical strategy and roadmap decisions for large-scale ranking, auction, or ML-driven production systems
- Expertise 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 problem scope and deliver measurable outcomes
- 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 such as risk assessment, bias mitigation, and quality reviews
- Demonstrated ability to integrate AI tools to optimize or redesign engineering workflows and drive measurable impact
- Experience building shared infrastructure and platforms focused on reliability, developer productivity, and efficiency
- Knowledge of 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 influenced revenue and user experience metrics at 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 and staying current with emerging AI technologies
- Experience measuring long-term effects of product changes including holdouts, long-term causal inference, and user learning effects

About you
You are a strategic and technical leader passionate about building high-impact ads systems that balance business goals with user experience. You thrive in ambiguous, high-impact environments and have a strong commitment to technical excellence, rigorous experimentation, and ethical AI practices. You enjoy collaborating across teams and mentoring engineers to grow as technical leaders.

Qualifications
Relevant advanced degree or equivalent experience in computer science, engineering, or related fields is preferred. Proven track record of managing and developing engineering teams in complex, large-scale production environments is essential.