Software Engineer, Machine Learning

AppLovin is seeking a Software Engineer with strong machine learning expertise to advance user signal and recommendation technologies across our advertising platform, which reaches more than 1 Billion users globally. In this role, you will work on large-scale machine learning problems spanning user signals, representation learning, ranking, retrieval, model architecture, and optimization. In this role, you will work on large-scale machine learning problems spanning user signals, representation learning, ranking, retrieval, model architecture, and optimization.

You will develop new ways to understand, represent, and utilize user signals and apply them to ranking and recommendation models. You will work across the ML stack from user signal and feature development to modeling, experimentation, and production to improve the relevance and performance of our advertising systems at scale.

• 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.
• Scale machine learning models and training systems to support increasing data volume, model complexity, and computational requirements.
• Improve training and inference efficiency by identifying bottlenecks across model computation, data loading, memory utilization, distributed execution, and hardware utilization.
• Build scalable tools and frameworks for user signal and feature evaluation, model training, experimentation, deployment, monitoring, and debugging.
• Design and analyze offline and online experiments to understand the incremental value of user signals and model improvements and their impact on product and business outcomes.
• Work closely with engineering, data, and product teams to bring new user signals and machine learning approaches from experimentation into production.

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