Software Engineer, Machine Learning
AppLovin, Palo Alto, CA
What you will do
- Develop and improve user signals, features, and representations used by large-scale machine learning models for advertising and recommendation.
- Explore machine learning approaches to learn effectively from large-scale, sparse, noisy, and heterogeneous user signals.
- Improve the quality, coverage, and utilization of user signals, and measure their impact on downstream machine learning models and advertising performance.
- Develop user representations and modeling approaches that effectively incorporate user signals into ranking, retrieval, prediction, and optimization systems.
- Advance large-scale recommendation systems across candidate retrieval, ranking, prediction, and optimization.
- Explore new model architectures and learning approaches to improve recommendation quality and advertising performance.
- Develop scalable approaches for representation learning, feature interaction, and multi-task learning across large-scale user signals.
- Identify and solve challenging ML problems spanning user signal quality, feature quality, model quality, training stability, data integrity, and serving performance.