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Meta’s Monetization RAI Research team seeks a research scientist to advance generative sequence modeling for recommender systems. You will model user and ad content and historical interactions as sequences to push personalization and relevance, influencing Meta’s monetization strategies.
Based in Bellevue, WA, you will collaborate across teams, publish in top conferences, lead end-to-end projects, and help scale large-scale data processing while exploring AGI-driven insights for future products.
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.
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
Minimum 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
Preferred Qualifications:
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
$184,000/year to $257,000/year + bonus + equity + benefits
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