<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 Python and modern ML frameworks (PyTorch).</li> <li>Experience with large-scale data and experimentation.</li> </ul> <p> </p> <div><strong>Nice to Have</strong></div> <ul> <li>Publications in top venues (NeurIPS, ICML, ICLR, KDD, RecSys, SIGIR, WWW).</li> <li>Experience with sequential modeling, representation learning, or causal inference.</li> <li>Knowledge of online experimentation and evaluation methodologies.</li> <li>Industry experience deploying ML in production systems.</li> </ul> <div> </div> <h2>What You’ll Gain</h2> <ul> <li>The opportunity to <strong>shape the next generation of recommendation science</strong>.</li> <li>A research culture that combines <strong>academic rigor with industrial scale</strong>.</li> <li>Access to world-class infrastructure and live experimentation loops.</li> <li>A collaborative environment that rewards both innovation and execution.</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>AppLovin provides a competitive total compensation package with a pay for performance rewards approach. Total compensation at AppLovin is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Depending on the position offered, equity, and other forms of incentive compensation (as applicable) may be provided as part of a total compensation package, in addition to dental, vision, and other benefits.</p> <p><strong>Other Types of Pay:</strong> Equity eligible</p> <p><strong>Health Insurance: </strong>Medical, Dental, Vision, Life, Disability</p> <p><strong>Retirement Benefits:</strong> 401(k) Retirement Plan</p> <p><strong>Paid Time Off:</strong> Unlimited Discretionary Time Off</p> <p><strong>Paid Holidays:</strong> 10 paid holidays per year</p> <p><strong>Paid Sick Leave:</strong> 80 hours per year</p> <p><strong>Method of Application:</strong> Apply online</p> <p><strong>Application Window:</strong> The application window is expected to close within 30 days of the posting date.</p> <p>All questions or concerns about this posting should be directed to peopleops@applovin.com.</p></div><div class="title">CA Base Pay Range</div><div class="pay-range"><span>$252,000</span><span class="divider">—</span><span>$400,000 USD</span></div></div></div><div class="content-conclusion"><div>AppLovin has become aware of a scam targeting jobseekers with fake “app optimization” and similar roles.  We do not ask our candidates to download apps or make any form of payment(s). AppLovin works with applicants through our Careers page and <a class="c-link" href="http://applovin.com/" target="_blank" data-stringify-link="http://applovin.com" data-sk="tooltip_parent">applovin.com</a> email addresses. If you are contacted through other unofficial channels (such as WhatsApp or Telegram) or asked to download an app or make a payment, these contacts are not legitimate. Confirm the information <a class="c-link" href="https://www.applovin.com/jobs" target="_blank" data-stringify-link="https://www.applovin.com/jobs" data-sk="tooltip_parent">here</a> and <a class="c-link" href="mailto:jobs@applovin.com" target="_blank" data-stringify-link="mailto:jobs@applovin.com" data-sk="tooltip_parent">contact us</a> directly with any questions.</div> <div> </div> <div>AppLovin is proud to be an equal opportunity employer that is committed to inclusion and diversity. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or other legally protected characteristics. Learn more about EEO rights as an applicant <a class="postings-link" href="https://www.eeoc.gov/employers/eeo-law-poster" target="_blank">here</a>.</div> <div> </div> <div>If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send us a request at accommodations@applovin.com.</div> <div> </div> <div>AppLovin will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law.  If you’re applying for a position in California, learn more <a class="postings-link" href="https://calcivilrights.ca.gov/fair-chance-act/" target="_blank">here</a>.</div> <div> </div> <div>To support an efficient and fair hiring process, we may use technology-assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers.</div> <div><hr></div> <div>Please read our <a class="postings-link" href="https://legal.applovin.com/global-applicant-privacy-notice/" target="_blank">Global Applicant Privacy Notice</a> to learn more about how AppLovin processes your personal information. </div> <div> </div> <div> </div> <div> </div> <div> </div> <div><img style="max-width: 100%;" src="https://storage.googleapis.com/applovin_assets_us/svg/greenhouse-icon-follow-us.svg" alt="Follow Us" width="118">     <a href="https://www.linkedin.com/company/applovin" target="_blank"><img style="max-width: 100%;" src="https://storage.googleapis.com/applovin_assets_us/svg/greenhouse-icon-linkedin.svg" alt="LinkedIn icon" width="28"></a>    <a href="https://x.com/AppLovin" target="_blank"><img style="max-width: 100%;" src="https://storage.googleapis.com/applovin_assets_us/svg/greenhouse-icon-x.svg" alt="X icon" width="28"></a>    <a href="https://www.instagram.com/applovin/" target="_blank"><img style="max-width: 100%;" src="https://storage.googleapis.com/applovin_assets_us/svg/greenhouse-icon-instagram.svg" alt="Instagram icon" width="28"></a></div></div>

Foundational Research Scientist

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