Research Scientist Intern, Agent for Recommendation (PhD)

Job description

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 Scientist Interns to join the Agent for Recommendation team within Facebook.

Our recommendation systems help billions of people discover content every day on products such as Facebook Reels and Feed. We build AI agents for recommendation that work alongside researchers and engineers to analyze data, develop models, debug issues, run experiments, and decide what to launch.

A key direction is recursive self-improvement (RSI)

closing the loop so that agents propose, test, and learn from improvements to production models, and get better at it with every iteration. This raises open research questions in agent reasoning and planning, grounding in domain knowledge and code, idea generation, memory and learning from feedback, evaluation, and human-AI collaboration, on frontier systems such as large-scale generative recommenders. Interns will own one focused research problem at the intersection of LLM agents, machine learning, and recommender systems, selected based on their expertise and team priorities.

They will build and evaluate solutions on production-scale systems, with a path to both product impact and publication. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

• Perform research to advance agentic AI and its application to large-scale recommender systems
• Design, build, and evaluate LLM-based agents for recommendation, such as agents that analyze data and experiments, diagnose issues, or develop and tune models Advance self-improvement loops in which agents propose, run, and learn from experiments, with people reviewing key decisions
• Develop methods that make agents reliable, such as grounding in domain knowledge and code, planning and tool use, memory, and learning from feedback
• Build rigorous evaluation for agents, including benchmarks, metrics, and measurement of real product impact
• Collaborate with researchers, engineers, and cross-functional partners, and communicate research plans, progress, and results Publish research results and contribute to research that can be applied to Meta product development

Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field

• Must obtain work authorization in 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 Experience conducting research in machine learning, recommender systems, large language models or agents, or reinforcement learning Experience designing experiments, analyzing results, and communicating technical findings Intent to return to the degree program after the completion of the internship/co-op
• Proven track record of research achievements as demonstrated by publications at leading venues such as NeurIPS, ICML, ICLR, KDD, RecSys, WWW, SIGIR, ACL, or EMNLP Research or project experience in LLM agents and tool use, reasoning and planning, automated machine learning, AI for research, reinforcement learning, or Bayesian optimization Expertise in recommender systems, ranking, relevance, or personalization, such as generative recommendation, sequential modeling, retrieval, or learning to rank
• Demonstrated ability to integrate AI tools to redesign workflows and drive measurable impact, and to stay current with emerging AI technologies (e.g., context engineering, agent orchestration) Experience working and communicating cross-functionally in a fast-paced team environment