We are the Monetization Ranking AI Research organization, dedicated to delivering personalized ads that maximize both user utility and advertiser value. We focus on advancing AI and ML technologies for all aspects of Monetization, including ranking, retrieval, model architecture, and optimization. By consistently integrating cutting-edge AI/ML 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 motivated AI specialists to join our team and drive SOTA research across the Monetization organization.

MINIMUM QUALIFICATIONS:

• Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 2+ years of experience holding an industry, faculty, or government researcher or applied researcher position, or related position
• Research experience in deep learning, reinforcement learning, natural language processing, computer vision, recommendations, ranking, search, or related areas
• 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:

• Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward.
• Experience working and communicating cross functionally in a team environment.
• First author publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, ACL).
• Master’s degree or PhD in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or relevant technical field.
• Experience taking ideas from research to production.

• 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 natural language processing and understanding
• Design and implement generative modeling solutions for data augmentation
• Research and develop graph-aware large language models
• Develop and deploy AutoML pipelines
• Apply Reinforcement Learning (RL) techniques, including long-term value optimization, RLHF, and RL4Reason
• Use causal learning to identify and understand the cause and effect of relationships across data
• 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