Meta’s Monetization pillar is at the cutting edge of delivering highly personalized ads that create maximum value for both users and advertisers. Within this pillar, the Ranking & AI (RAI) Research team drives state-of-the-art research initiatives, focusing on high-impact, high-risk projects—true moonshots—with the potential to redefine Meta’s monetization strategies. By consistently pushing the boundaries of what’s possible, we deliver breakthrough innovations that not only advance Meta’s business objectives but also result in publications at top-tier conferences. Inspired by recent breakthroughs in large language models (LLMs), the RAI Sequence Learning team is pioneering a transformative approach to recommender systems. We are reimagining recommendation as a generative sequence modeling problem, moving beyond traditional methods that treat recommendations as classification tasks on pairs. Instead, our approach models user and ad content, as well as historical interaction data, as sequences—unlocking new possibilities for personalization and relevance. As a research scientist on this team, you will play a pivotal role in shaping the future of technology and business at Meta, especially as we enter the era of artificial general intelligence (AGI). Your contributions will directly influence the trajectory of Meta’s monetization strategies and help define the next generation of recommender systems.
Responsibilities
Extracting meaningful signals from both 1st-party and 3rd-party data sources Advancing representation learning Scaling solutions to efficiently process hundreds of billions of data points Driving continuous algorithmic innovation Seamlessly productionizing research breakthroughs all while optimizing serving costs
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD in Computer Science, Machine Learning, or a relevant technical field 3+ years of industry research experience in LLM/NLP, computer vision, or related AI/ML model training Experience as a technical lead on a team and/or leading complex technical projects from end-to-end Publications at peer-reviewed conferences (e.g. ICLR, NeurIPS, ICML, KDD, CVPR, ICCV, ACL) Programming experience in Python and hands-on experience with frameworks such as PyTorch Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment A track record of impactful research in the ranking/retrieval/recommendation space, as demonstrated by publications, open-source contributions, or real-world deployments First-authored publications at peer-reviewed conferences (e.g. ICLR, NeurIPS, ICML, KDD, CVPR, ICCV, ACL) Experience in pre-training, post-training, fine-tuning models Experience in causal learning, sequence learning, classification, neural networks, graph learning, items associated, in-depth content understanding (user behavior, user interaction) Experience solving complex problems and comparing alternative solutions, tradeoffs, and broad points of view to determine a path forward Willing to collaborate with others in a productive, interdisciplinary environment