Research Scientist Intern, Generative Recommendation Systems (PhD)

Meta is seeking Research Interns to join our research team within Meta Recommendation Systems (MRS). The team brings together world-class AI Research Scientists and Engineers who are exploring and advancing state-of-the-art technology, centered on AI and ML, to build a unified infrastructure and model service that leapfrogs recommendation systems (RecSys), their theory and algorithms, across Meta’s “Family of Apps” (FoA), including Facebook, Instagram, and WhatsApp. Our interns will have the opportunity to perform cutting-edge research on AI for RecSys, with the potential to have an impact at Meta’s scale.

Our vision is to leverage frontier AI models and technologies in all aspects of recommendation systems, including but not limited to deep understanding of multimodal content (especially images/videos, posts, and user interactions), modeling user interests and preferences, agentic AI for RecSys, recursive self-improvement for RecSys, and understanding the value of the ecosystem. We contribute to the mission of connecting users to the content they enjoy, are inspired by, and want to see more of. To this end, we conduct cutting-edge research using the complete suite of audio, visual, text, and metadata signals associated with posts and videos to improve recommendation relevance across all surfaces and provide a better, more meaningful experience to users. We build tools, create frameworks, and train models that we deploy together with product and infrastructure teams to gain adoption across the FoA. We also publish scientific papers to help advance the state of the art in all aspects of the technology stack.

Our team offers twelve (12) to twenty-four (24) weeks long internships and we have various start dates throughout the year.

Responsibilities

Initiate and lead efforts toward long-term, ambitious research goals while identifying intermediate milestones, including rethinking the architecture and underlying logic of recommendation systems around generative recommendation models, and advancing research on the generative recommendation paradigm across diverse product use cases. Conduct original research that can eventually be applied to Meta product development, and engage with the wider research community, including publishing papers and releasing open source software where appropriate. Design, train, and support AI/ML libraries and models, and continuously improve the performance of generative recommendation models across key directions such as model scaling, reinforcement learning (RL)-based preference alignment, and LLM/MLLM knowledge alignment. Collaborate with researchers and cross-functional partners, including communicating research plans, progress, and results.

Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, or a relevant technical field Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment Strong passion for AI and belief in its power to drive business and societal value, including actively following the frontier of large recommendation models and large language models and staying current with industry developments Experience with Python and machine learning/deep learning libraries such as PyTorch and Transformers Familiarity with AI/ML modeling and emerging technologies such as agentic AI and recursive self-improvement (RSI) Experience building systems based on machine learning and/or deep learning methods Intent to return to the degree program after the completion of the internship Proven track record of achieving significant results, as demonstrated by grants, fellowships, patents, or publications at leading workshops or conferences such as NeurIPS, ICLR, KDD, ICML, SIGIR, WSDM, RecSys, CIKM, CVPR, ECCV/ICCV/WACV, ACL, EMNLP, ICASSP, CoLM, or similar venues Experience working and communicating cross-functionally in a team environment Prior research or project experience in sequence modeling, recommendation systems, user modeling, reinforcement learning, or related areas Prior research or project experience with LLMs (e.g., pre-/mid-training, alignment, post-training, RLVR) and agents (e.g., meta-harness, self-evolving agents) Demonstrated software engineering experience via an internship, work experience, coding competitions, or widely used contributions to open source repositories (e.g., GitHub)