AppLovin

This role focuses on foundational research for new recommendation models and paradigms, leveraging rich live user data and large-scale compute. It's an academic-industrial hybrid research group aiming to shape the next generation of recommendation science and deploy work into real products.

Applied Research Scientist role at AppLovin, focusing on building ML/AI systems for adtech, specifically ads delivery and recommendation systems. The role involves processing large-scale data, integrating multimodal data, and running A/B tests to drive business results in a fast-paced environment.

Machine Learning Engineer focused on building and optimizing large-scale recommendation systems for a social media platform, involving recall, ranking, CTR prediction, and multi-objective optimization to drive user engagement and growth.

Research Scientist role focused on optimizing and improving machine learning models for ad tech, aiming to enhance ad delivery effectiveness and efficiency. Responsibilities include developing and refining ML models, collaborating on data pipelines and inference performance, staying updated on ML and ad tech advancements, identifying optimization opportunities, and designing experiments. Requires a Master's or PhD in a relevant field, strong ML fundamentals, and experience with model optimization and inference performance.