Foundational Research Scientist

<div class="content-intro"><h3><span style="font-weight: 400;"><strong>About AppLovin</strong></span></h3> <p>AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: <a href="http://www.applovin.com/">www.applovin.com</a>.</p> <p><span style="font-weight: 400;">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.</span></p> <p>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 <a href="https://www.applovin.com/jobs/">HERE</a>.</p></div><h2>About Us</h2> <div><br>At AppLovin, we’re powering the future of product discovery and engagement through cutting-edge machine learning. <strong>Recommender systems quietly shape the daily lives of billions of people</strong> — deciding what we watch, read, play, and buy. They don’t just influence culture; they drive <strong>trillions of dollars in market value across ads, commerce, and streaming</strong>, fueling <strong>economic growth and job creation worldwide</strong>, and the space is still growing at <strong>double-digit rates annually</strong>.</div> <div> </div> <div>The <strong>modern recommendation stack was established about a decade ago</strong>, but we believe the next era of models will look fundamentally different. We’re assembling a research team dedicated to shaping that future.</div> <div> </div> <h2>The Opportunity</h2> <div><br>We’re creating a world-class <strong>academic-industrial hybrid research group</strong> 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 <strong>and</strong> see your ideas transform how the world discovers content.</div> <div> </div> <h2>What You’ll Do</h2> <ul> <li>Drive foundational research to create new recommendation models and paradigms.</li> <li>Leverage <strong>rich live user data</strong> and <strong>large-scale compute</strong> to validate models rapidly.</li> <li>Collaborate closely with engineering and product teams to operationalize research.</li> <li>Publish findings and contribute to the broader ML and RecSys community.</li> </ul> <div> </div> <h3>Benefits of Research in Industry</h3> <ul> <li><strong>Rich real-time data</strong>: access to large-scale, diverse, and dynamic user interactions.</li> <li><strong>Massive compute infrastructure</strong>: GPU clusters, feature stores, deployment pipelines.</li> <li><strong>Rapid experimentation</strong>: immediate feedback through A/B testing and online evaluation.</li> <li><strong>Direct impact</strong>: see your models shape user experiences and business outcomes.</li> <li><strong>Cross-disciplinary collaboration</strong>: partner with product, design, and engineering teams.</li> <li><strong>Balanced path</strong>: combine scientific exploration with practical deployment.</li> </ul> <div> </div> <h2>Who You Are</h2> <div><br>We’re looking for <strong>rising researchers</strong> with strong academic backgrounds and a desire to have real-world impact.<br> </div> <div><strong>Minimum Qualifications</strong></div> <ul> <li>PhD (or equivalent research experience) in CS, ML, Statistics, or related field.</li> <li>Strong background in deep learning.</li> <li>Proven track record of research excellence (publications, awards, impactful projects).</li> <li>Proficiency in P ... (truncated, view full listing at source)