Ads is the largest revenue generator at Meta and Ads Quality represents around 20% of total revenues which are used to generate long term ads and organic engagement. Core Ads Quality is a unique team jointly optimizing for both quality and revenue, aiming at making this investment more revenue / quality trade-off efficient and generate long term revenue growth through user learning. Among others, Core Ads Quality focuses on: * Finding the right trade-off between short and long term revenues * Standardising and optimise quality treatment of ads across surfaces and page types * Understanding user behaviour with respect to ads quality * Building a solid infrastructure around signals, labels and quality metrics We work at the intersection of Ads, Machine Learning and User Behaviour understanding. The nature of our work is very analytical, with a solid collaboration with our Data Scientist and a heavy focus on not only understand “what” but also “why”. Despite having been created a couple of years ago, the Ads Quality space at Meta is still nascent and full of unexploited opportunities. The org is further structured into the following teams/sub-pillars: * Integrity & Efficiency: Proactively cover long-term revenue risks from advertiser friction while supporting XI with delivery expertise. * Ads Conversion Familiarity: Accelerate Non-Purchaser (NP) -> Purchaser (P) transition by increasing familiarity of ads for users who don't interact with ads frequently * Post-Click Quality: Stop Purchaser (P) - >Non purchaser (NP) user conversions from bad purchase experiences. * Modelling: Enhance quality and drive long-term revenue growth through modelling. * Quality Science: Build the foundational end to end understanding for funnel quality signals to ensure its the efficiency, health and coverage. The team has consistently hit their goals and delivered XXXM$ in incremental long term revenue for Meta while ensuring high ads quality.
Responsibilities
Drive the team's goals and technical direction to pursue opportunities that make your larger organization more efficient Effectively communicate complex features and systems in detail Understand industry and company-wide trends to help assess & develop new technologies Partner and collaborate with organization leaders to help improve the level of performance of the team and organization Identify new opportunities for the larger organization and influence the appropriate people for staffing/prioritizing these new ideas Lead long term technical strategy and roadmap for large cross-company efforts Suggest, collect and synthesize requirements and create an effective feature and technology roadmap
Qualifications
Experience developing machine learning algorithms or machine learning infrastructure in Python, PyTorch, and/or C/C++ Bachelor in Artificial Intelligence (AI), computer science, related technical fields, or equivalent practical experience Experience in bringing research results into production Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term mission Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions xperience communicating and working across functions to drive solutions Experience in manipulating and analyzing complex, high-volume data from varying sources Large experience with machine learning / AI technologies PhD in Artificial Intelligence (AI), computer science, related technical fields, or equivalent practical experience Experience in Reinforcement Learning, GenAI, Large Language Models, etc Experience in Ads, especially in auction theory and implementation (bidding, budgeting, targeting) Experience in User Behaviour modellling, Long-term Value optimization or Causal Learning