# Research Scientist Intern - Ads Quality - PhD at Meta

- Company: [Meta](https://meta.com)
- Location: Menlo Park, CA
- Type: Internship · Internship
- Posted: 2026-11-03
- Apply: https://www.metacareers.com/jobs/1838454487574865
- Page: https://newgrad.ai/jobs/research-scientist-intern-ads-quality-phd-a871d722

## Summary by newgrad.ai

What you'll do:

- Develop machine learning solutions for ad recommendation systems.
- Model long-term user outcomes using causal inference and reinforcement learning.
- Collaborate with engineers to optimize ad ranking and user experience.

What they're looking for:

- PhD students with expertise in machine learning or related fields.
- Ability to frame complex recommendation challenges as rigorous research problems.
- Interest in sequence modeling and large-scale data systems.

Pay and perks:

- Internship durations of 12, 16, or 24 weeks.
- Flexible start dates available throughout the year.

## Posting

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 the Ads Quality team within Facebook Ads Recommendations. Ads Quality is the "user value" team in Facebook Monetization: our mission is to deliver long-term value for both people and businesses by showing ads people love. Every time someone opens or scrolls Facebook, we help decide which ads to show, how many, and where, so that ads create value for advertisers without eroding the experience that brings people back. Our interns frame practical, high-impact problems in ads recommendation as rigorous machine learning problems, and bring their own expertise to build new solutions with guidance from our research scientists and software engineers.

More concretely, today's ranking systems score each ad on its immediate outcome, such as a click or a conversion. But every impression and engagement also changes what a person does next: a relevant ad can build interest and make future ads more valuable, while a poor one can shorten a session or push people toward ignoring ads altogether. Learning this long-term value is hard. Long-horizon outcomes are sparse and delayed, the value "without the event" is never observed, user actions cannot be randomized, and ads and organic content affect each other. If you are excited about combining causal inference, sequence modeling, and reinforcement learning to teach a recommender system the long-term consequences of its decisions, at the scale of billions of people, come join us!

Our internships are twelve (12) to sixteen (16), or 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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