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
Minimum 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