Meta is seeking a Research Engineer to join our Meta Recommendation Systems (MRS) AI Algorithm Team. Join us to build Meta’s User Intelligence Engine — a unified platform that models who the user is, what they need, and why they act by integrating state, representation, reasoning, and multi-architecture modeling to power Meta’s Recommendation System with personalized, context-aware experiences across the ecosystem. We’re bringing together two powerhouses: - Generative AI/LLMs for semantic understanding and reasoning - Meta’s world-class ads & organic ranking expertise for optimized decision-making at scale As part of a rapidly growing ML team, you’ll shape the next generation of User Understanding models and Meta Recommendation Systems, delivering personalization that feels intuitive, adaptive, and truly human.
Responsibilities
Develop and implement large-scale model architectures, leveraging model scaling and transfer learning techniques Prioritize training scalability and signal scaling to optimize model performance, efficiency, and reliability Develop and apply NextGen sequence learning techniques to drive advancements in recommender systems and machine learning Design and implement generative modeling solutions for data augmentation Develop and deploy machine learning pipelines Develop and implement innovative solutions for data-related challenges, utilizing knowledge of semi/self-supervised learning, generative techniques, sampling, debiasing, domain adaptation, continual learning, data augmentation, cold-start, content understanding, and large language models
Qualifications
Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta Research experience in machine learning, deep learning, and/or recommender systems, natural language processing Programming experience in Python and hands-on experience with frameworks such as PyTorch Exposure to architectural patterns of large scale software applications Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Master's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience A PhD in AI, computer science, data science, or related technical fields First author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR, RecSys, SIGIR, KDD, WSDM, TheWebConf, ICDM, ACL, EMNLP, NAACL, AAAI, ICCV, CVPR) Direct experience in generative AI, LLMs, RecSys, ML research Experience with developing large-scale machine learning models from inception to business impact