Research Engineer, Monetization AI

We are the Monetization Ranking and Foundational AI organization, dedicated to delivering personalized ads that maximize both user utility and advertiser value. We focus on advancing AI, ML and RecSys technologies for all aspects of Monetization, including ranking, retrieval, model architecture, and optimization. By consistently integrating cutting-edge AI/ML/RecSys advancements, we help Meta’s products achieve long-term goals and have contributed tens of billions in revenue. With our growing impact, we’re seeking AI/ML/RecSys specialists to join our team and drive SOTA research and production across the Monetization organization.

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 Collaborate with cross-functional teams to design and optimize ML systems, leveraging expertise in hardware-software co-design, including quantization, compression, and resource-efficient AI, to drive performance improvements and efficiency gains 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

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Research experience in machine learning, deep learning, natural language processing, and/or recommender systems Experience with developing machine learning models at scale from inception to business impact Programming experience in Python and hands-on experience with frameworks such as PyTorch Exposure to architectural patterns of large scale software applications PhD in AI, Computer Science, Data Science, or related technical fields Master's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience First author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR, ICCV, CVPR, ACL, EMNLP, RecSys, KDD, WSDM, TheWebConf, ICDM, AAAI) Direct experience in generative AI, LLMs, RecSys, ML research 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