Research Scientist Intern - Meta Recommendation Systems - Core ML (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 research team within Meta Recommendation Systems (MRS). This team brings together a world-class team of AI Research Scientists and Engineers.

The team is exploring and advancing SoTA technology, centering around AI and ML, to arrive at a unified infrastructure and model service to leapfrog recommendation systems (Recsys), its theory and algorithms across Meta (Facebook, Instagram and WhatsApp, the “Family of Apps” (FoA)).

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 AI frontier models and technology in all aspects of recommendation systems including but not limited to deep understanding of multi-modal content (especially images/videos, posts, user interactions), modeling user interests and preference, agentic AI for recsys, recursive self-improvement for recsys, values of the ecosystems.

We contribute to the mission of connecting users to the content they enjoy, are inspired by, and that they 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.