About this role
Meta is seeking a Visiting Scholar to join our feed modeling team as a short-term employee (STE). In this role, you will conduct research and contribute to advancing machine learning capabilities, particularly in areas related to post-training, feed ranking, recommendations, and content understanding. You will collaborate closely with research scientists and engineers to explore novel approaches and translate research findings into practical applications. This is an opportunity to engage in cutting-edge AI research at Meta.
Responsibilities
Conduct original research in machine learning, with a focus on post-training optimization, model alignment, or related AI research areas
Design and run experiments to evaluate novel approaches and analyze results
Publish research findings at top-tier academic venues
Collaborate with research scientists and engineers to translate research into production-ready systems at Meta scale
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
Currently has a Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
3+ years of conducting research in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field in either industry or academia
Demonstrated publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, RecSys)
Experience with machine learning research, including designing experiments and analyzing results
Experience with ML frameworks such as PyTorch, TensorFlow, or equivalent
Experience with Python or another programming language used for ML research Experience applying ML to natural language understanding, computer vision, or multimodal learning problems
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience with large-scale distributed training or model serving infrastructure for deep learning models
Publication record at top-tier venues such as NeurIPS, ICML, or ICLR, with evidence of research impact (e.g., citation count, best paper awards, or invited talks)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience running and analyzing A/B experiments to measure the impact of ML model changes on product metrics
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Familiarity with ranking, recommendation, or personalization systems and the ML techniques commonly applied in those domains