Research Scientist Intern, Generative Recommendation Systems (PhD)

Meta • Menlo Park, CA

2d • $7,650 - $12,667

About The Position

Meta is seeking Research Interns to join its Meta Recommendation Systems (MRS) research team. MRS brings together AI research scientists and engineers to advance AI and machine learning technology and build unified infrastructure and model services for recommendation systems across Meta’s Family of Apps, including Facebook, Instagram, and WhatsApp. Interns conduct cutting-edge research on AI for recommendation systems, with the potential to affect products at Meta’s scale.

The team’s research explores frontier AI models and technologies for recommendation systems, including multimodal content understanding, modeling user interests and preferences, agentic AI, recursive self-improvement, and ecosystem value. It uses audio, visual, text, and metadata signals associated with posts and videos to improve recommendation relevance and user experience. The team builds tools, frameworks, and models for deployment with product and infrastructure teams, and publishes scientific research. Internships last 12 to 24 weeks, with various start dates throughout the year.

Requirements

• Currently has or is in the process of obtaining a Ph.D. in Computer Science, Artificial Intelligence, or a relevant technical field.
• Must obtain work authorization in the country of employment by the time of hire and maintain work authorization throughout employment.
• Strong interest in AI and its potential business and societal value; actively follows developments in large recommendation models and large language models.
• 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.

Nice To Haves

• Intent to return to the degree program after completing the internship.
• Track record of significant results demonstrated through grants, fellowships, patents, or publications at leading workshops or conferences, including 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.
• Research or project experience in sequence modeling, recommendation systems, user modeling, reinforcement learning, or related areas.
• Research or project experience with LLMs, including pre-/mid-training, alignment, post-training, or RLVR, and agents such as meta-harness or self-evolving agents.
• Software engineering experience through an internship, work experience, coding competitions, or widely used contributions to open-source repositories such as GitHub.

Responsibilities

• Initiate and lead efforts toward long-term research goals, identifying intermediate milestones and rethinking recommendation-system architecture and underlying logic around generative recommendation models.
• Advance research on the generative recommendation paradigm across diverse product use cases.
• Conduct original research with potential application to Meta products, engage with the broader research community, and publish papers or release open-source software where appropriate.
• Design, train, and support AI/ML libraries and models.
• Improve generative recommendation model performance in areas such as model scaling, reinforcement-learning-based preference alignment, and LLM/MLLM knowledge alignment.
• Collaborate with researchers and cross-functional partners, communicating research plans, progress, and results.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree