Summary: 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 metricsWe 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 organization 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. Required Skills: Research Scientist, AI/ML (PhD) Responsibilities: 1. Apply relevant AI and machine learning techniques to build Ads auction/user behaviour treatments 2. Develop novel, accurate AI algorithms and advanced systems for large scale applications 3. Work towards long-term ambitious production goals, while identifying intermediate milestones 4. Directly contribute to experiments, including designing experimental details, developing reusable code, running evaluations, and organizing results 5. Work with large data, and contribute to development of large scale foundation models 6. Design methods, tools, and infrastructure to push forward the state of the art in AI Minimum Qualifications: Minimum Qualifications: 7. Currently has or is in the process of obtaining a PhD in Artificial Intelligence (AI), Computer Science, or a related technical field 8. Experience developing machine learning algorithms or machine learning infrastructure in Python, PyTorch, and/or C/C++ 9. Experience in training, fine-tuning, and/or experimenting with foundation models beyond black-box use Preferred Qualifications: Preferred Qualifications: 10. Experience in Reinforcement Learning, GenAI, Large Language Models, etc 11. Experience in Ads, especially in auction theory and implementation (bidding, budgeting, targeting) 12. Experience in User Behaviour modellling, Long-term Value optimization or Causal Learning Industry: Internet