Research Engineer, Monetization AI

Meta•Bellevue, WA

1d•$154,003 - $217,000

About The Position

The Monetization Ranking AI Research organization develops personalized advertising systems that maximize user utility and advertiser value. The team advances AI and machine learning across monetization, including ranking, retrieval, model architecture, and optimization, integrating new research to support Meta’s long-term product goals. The Research Engineer will conduct state-of-the-art research and build scalable, efficient, and reliable AI/ML systems for monetization.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
At least 2 years of experience in an industry, faculty, or government researcher or applied researcher position, or a related position.
Research experience in deep learning, reinforcement learning, natural language processing, computer vision, recommendations, ranking, search, or a related area.
Programming experience in Python and hands-on experience with frameworks such as PyTorch.
Must obtain work authorization in the country of employment by the time of hire and maintain ongoing work authorization during employment.

Nice To Haves

First-author publications at peer-reviewed AI conferences such as NeurIPS, CVPR, ICML, ICLR, ICCV, or ACL.
Experience partnering with engineering, product, and data science teams to define requirements, align on priorities, and deliver ML solutions.
Experience taking ideas from research to production.
Experience solving complex problems and comparing alternative solutions, tradeoffs, and perspectives to determine a path forward.
Demonstrated ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Experience implementing responsible and ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Ongoing AI skill development, such as prompt/context engineering or agent orchestration, and staying current with emerging AI technologies.
Master's degree or PhD in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or a relevant technical field.

Responsibilities

Develop and implement large-scale model architectures using model scaling and transfer learning techniques.
Prioritize training scalability and signal scaling to optimize model performance, efficiency, and reliability.
Develop and apply next-generation sequence learning techniques for 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 techniques, including long-term value optimization, RLHF, and RL4Reason.
Use causal learning to identify and understand cause-and-effect relationships across data.
Collaborate with cross-functional teams to design and optimize ML systems, applying hardware-software co-design expertise such as quantization, compression, and resource-efficient AI to improve performance and efficiency.
Develop and implement solutions to data-related challenges using techniques and knowledge including semi-supervised and self-supervised learning, generative techniques, sampling, debiasing, domain adaptation, continual learning, data augmentation, cold-start, content understanding, and large language models.