Research Engineer, Monetization AI

Meta•Sunnyvale, CA

6d•$88 - $257,000

About The Position

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.

Requirements

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

Nice To Haves

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

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
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