Foundational Research Scientist

About AppLovin AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end advertising solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com. To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others. Fortune recognizes AppLovin as one of the Best Workplaces in the Bay Area, and the company has been a Certified Great Place to Work for the last four years (2021-2024). Check out the rest of our awards HERE. About Us At AppLovin, we’re powering the future of product discovery and engagement through cutting-edge machine learning. Recommender systems quietly shape the daily lives of billions of people — deciding what we watch, read, play, and buy. They don’t just influence culture; they drive trillions of dollars in market value across ads, commerce, and streaming, fueling economic growth and job creation worldwide, and the space is still growing at double-digit rates annually. The modern recommendation stack was established about a decade ago, but we believe the next era of models will look fundamentally different. We’re assembling a research team dedicated to shaping that future. The Opportunity We’re creating a world-class academic-industrial hybrid research group to advance recommender systems. Unlike academia, your work won’t live only in papers — it will be deployed into real products, used by millions, and validated at scale. This is your chance to push the science forward and see your ideas transform how the world discovers content. What You’ll Do Drive foundational research to create new recommendation models and paradigms. Leverage rich live user data and large-scale compute to validate models rapidly.