Research Scientist Intern, Ranking & Recommendations (PhD)

Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.

Meta is seeking Research Interns to join our Post-Training & Research team within Facebook. The team is part of Facebook Discovery, which builds the systems that decide which posts, photos, videos and Reels people see on Facebook. Our team works on ranking and recommendation, content understanding, and post-training of the AI models behind agentic experiences on Facebook. Our interns will have the opportunity to turn open problems in these systems into research questions and work on them with our research scientists and engineers, with the potential for impact on billions of people.

We believe large language models (LLMs) and AI agents will change how recommendation systems are built. We study generative recommendation models that are trained like language models on sequences of user activity. We build ranking models that reason over a person’s history and a set of candidates to rank and compose what that person sees. We use multimodal models to understand the quality and originality of posts and videos. We post-train models with supervised fine-tuning and reinforcement learning from user feedback, both for recommendation and for AI agents on Facebook. We deploy our models together with product and infrastructure teams, and we publish our results to advance the state of the art.

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

Responsibilities

Define and lead a research project in machine learning for ranking and recommendation, with clear milestones over the course of the internship.
Design, train and post-train deep learning models, from recommendation models to LLMs and AI agents, on Facebook-scale data.
Evaluate models with offline metrics and online experiments, and analyze the results to decide the next iteration.
Publish research results and contribute to research that can be applied to Meta product development.
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, Machine Learning, or relevant technical field
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
Experience with Python and deep learning frameworks such as PyTorch
Familiarity with modern AI/ML modeling techniques (e.g., Transformers, deep learning for recommendation, LLMs and LLM reasoning, multimodal models, mixture-of-experts, diffusion models, RAG, AI agents, post-training with SFT and RL)
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, as well as publications at leading workshops or conferences such as NeurIPS, ICLR, ICML, KDD, SIGIR, WSDM, RecSys, CIKM, ACL, EMNLP, CoLM, CVPR, ICCV, ECCV, or similar venues
Research experience in one or more of the following areas: LLM reasoning, post-training (SFT, reinforcement learning, reward modeling), AI agents, generative recommendation, diffusion models, ranking and recommendation systems, sequence and user modeling, computer vision, or multimodal content understanding
Experience training large-scale deep learning models, such as LLMs or recommendation models
Experience with C++ or with optimizing LLM training and inference
Demonstrated software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
Proficiency in using AI coding agents for rapid development and independent problem solving
Experience working and communicating cross functionally in a team environment