Machine Learning Engineer – Feed Recommendation

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. 【The Role】 We are looking for a Machine Learning Engineer with strong experience in large-scale recommendation systems to help build the next-generation social media platform. You will own critical components of our recommendation stack — including recall, ranking, CTR modeling, and multi-objective optimization — with the goal of driving retention, engagement, and long-term ecosystem growth. 【A Day in the Life】 Design and deploy scalable recommendation pipelines Develop and optimize CTR/CVR prediction models Improve multi-objective ranking strategies (retention, monetization, diversity, long-term value) Tackle cold-start challenges for new users and new content Run offline experiments and online A/B testing to drive measurable gains Collaborate closely with product, engineering, and monetization teams Continuously iterate on model performance, latency, and system reliability 【The Impact You’ll Make】 Improve user retention through intelligent content recommendation Drive measurable lift in engagement and monetization metrics Build core ranking mechanics beyond incremental model tuning Shape the foundation of a scalable, long-term content ecosystem 【Who You Are】 3–5 years of experience building production-grade ML systems Strong hands-on experience in recommendation systems Experience in one or more: ...