Foundational Research Scientist, Applovin

• We’re looking for rising researchers with strong academic backgrounds and a desire to have real-world impact
• PhD (or equivalent research experience) in CS, ML, Statistics, or related field
• Strong background in deep learning
• Proven track record of research excellence (publications, awards, impactful projects)
• Proficiency in Python and modern ML frameworks (PyTorch)
• Experience with large-scale data and experimentation

Desirable

• 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
• 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
• Drive foundational research to create new recommendation models and paradigms
• Leverage rich live user data and large-scale compute to validate models rapidly
• Collaborate closely with engineering and product teams to operationalize research
• Publish findings and contribute to the broader ML and RecSys community
• Benefits of Research in Industry:
• Rich real-time data: access to large-scale, diverse, and dynamic user interactions
• Massive compute & infrastructure: GPU clusters, feature stores, deployment pipelines
• Rapid experimentation: immediate feedback through A/B testing and online evaluation
• Direct impact: see your models shape user experiences and business outcomes
• Cross-disciplinary collaboration: partner with product, design, and engineering teams
• Balanced path: combine scientific exploration with practical deployment
• What You’ll Gain:
• The opportunity to shape the next generation of recommendation science
• A research culture that combines academic rigor with industrial scale
• Access to world-class infrastructure and live experimentation loops
• A collaborative environment that rewards both innovation and execution