Research Scientist Intern, Video Recommendations (PhD)

Research Scientist Intern, Video Recommendations (PhD) Meta is seeking AI Research interns to join our Video Recommendations team within Facebook. Videos (Reels, long videos, and Live streams) are increasingly the formats of choice across the social media industry, and making all these formats successful is a top priority for Facebook, and the core mission owned by this org. Time spent on Facebook watching videos accounts for 1.2+ Billion hours/day, which is over 56% of time spent globally on the App, and this number is expected to continue growing. This team is directly in charge of recommending all sorts of videos to users. In this role, you'll be working primarily with Facebook's video content, which is predominantly driven by our recommendation system. Your job will involve optimizing our system to provide users with the most engaging and high-quality content. Our interns will have the opportunity to participate in the process of framing practical, high-impact challenges in recommendation into rigorous machine learning problems, and bring their own expertise to build innovative solutions under the guidance from our Research Scientists and Software Engineers. More concretely, you will focus on solving end-to-end problems in the intersection of Product, ML and Infra, and serve as a bridge between high-demand business needs and long-term foundational investments via ML approach. Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Familiarity with machine learning and common deep learning architectures Intent to return to the degree program after the completion of the internship/co-opProven track record of solid research achievements as demonstrated by grants, fellowships, patents, as well as publications at leading AI conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP and KDDPrior research or project experience in one or more of the following areas: reinforcement learning, pre-training and supervised fine-tuning of large language models, sequence modeling, diffusion models, and their applications in recommendation systems Demonstrated software development experience via tech internships, work experience, coding competitions, or widely used contributions in open source machine-learning repositories Experience working and communicating cross functionally in a fast-paced team environment. Ideal candidates should have the ability to quickly understand and identify the research opportunities behind real-world applications, select the appropriate ML methods to explore, and proactively drive the iterations based on clear analysis of the current results