# Research Scientist Intern - Video Recsys at Meta

- Company: [Meta](https://meta.com)
- Location: Menlo Park, CA
- Type: Internship · Internship
- Posted: 2026-10-31
- Apply: https://www.metacareers.com/jobs/1553890293163197
- Page: https://newgrad.ai/jobs/research-scientist-intern-video-recsys-e985983d

## Summary by newgrad.ai

What you'll do:

- Build AI agents for recursive self-improvement in video recommendation models.
- Develop prototypes that automate model experimentation, evaluation, and data refinement.
- Translate complex recommendation challenges into rigorous machine learning solutions.

What they're looking for:

- Ability to work 12 to 24 weeks.
- Experience framing and solving machine learning problems.
- Proficiency in building end-to-end prototypes for model improvement.

## Posting

Meta is seeking AI Research interns to join our Video Recommendations team within Facebook. Videos (Reels, long videos, and Live streams) are increasingly the formats of choice across the social media industry, and making all these formats successful is a top priority for Facebook, and the core mission owned by this org. Time spent on Facebook watching videos accounts for 1.2+ Billion hours/day, which is over 56% of time spent globally on the App, and this number is expected to continue growing.

This team is directly in charge of recommending all sorts of videos to users, powered by our large-scale unified video recommendation models. In this role, you will work on Recursive Self-Improvement (RSI): building AI agents that autonomously propose, run, evaluate, and learn from improvements to these models, closing the loop between ML experimentation and model quality. Our interns will have the opportunity to frame practical, high-impact challenges in recommendation into rigorous machine learning problems, and bring their own expertise to build innovative solutions under the guidance of our Research Scientists and Software Engineers.

More concretely, you will develop an agent-driven RSI prototype that continuously proposes, evaluates, and refines improvements for a selected video recommendation task. The scope spans training data improvement, environment and experiment setup, automated offline model evaluation, and feedback collection from model quality signals such as normalized entropy (NE) and recall. You will use experiment outcomes to improve both the agent's intervention strategy and its evaluation criteria, and deliver a working end-to-end prototype with evidence against a fixed baseline. Work will be mentored by the team, with production changes and online experiments subject to owner review and approval.

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

From Meta's public posting, laid out for reading by newgrad.ai with the wording unchanged. Check the original before you apply.

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