Software Engineer, Machine Learning
AppLovin · Palo Alto, CA
About this role
**About AppLovin** AppLovin makes technologies that help businesses connect to their ideal customers. The company provides end-to-end advertising solutions to reach, monetize, and grow global audiences. Learn more at http://www.applovin.com. AppLovin is seeking a **Software Engineer (Machine Learning)** to advance user signal and recommendation technologies across an advertising platform that reaches **more than 1 billion users globally**. In this role, you’ll work on large-scale machine learning problems spanning **user signals, representation learning, ranking, retrieval, model architecture, and optimization**—developing new ways to understand, represent, and utilize user signals and applying them to ranking and recommendation models. --- **Responsibilities** - Develop and improve user signals, features, and representations used by large-scale ML models for advertising and recommendation. - Explore ML approaches to learn effectively from large-scale, sparse, noisy, and heterogeneous user signals. - Improve the quality, coverage, and utilization of user signals; measure impact on downstream ML models and advertising performance. - Develop user representations and modeling approaches that 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 across user signal quality, feature quality, model quality, training stability, data integrity, and serving performance. - Scale ML models and training systems for increasing data volume, model complexity, and compute requirements. - Improve training and inference efficiency by addressing bottlenecks in model computation, data loading, memory utilization, distributed execution, and hardware utilization. - Build scalable tools and frameworks for evaluation, training, experimentation, deployment, monitoring, and debugging. - Design and analyze offline and online experiments to understand incremental value and impact on product/business outcomes. - Partner with engineering, data, and product teams to bring new signals and ML approaches from experimentation into production. --- **Minimum Qualifications** - Bachelor’s degree in Computer Science, Computer Engineering, Machine Learning, or related field (or equivalent practical experience). - **4+ years** developing and deploying machine learning systems in production. - Experience with ML/deep learning in areas such as recommendation, ranking, retrieval, prediction, advertising, or representation learning. - Experience training ML models using large-scale datasets. - Strong ML fundamentals: model architectures, optimization, representation learning, feature engineering, and model evaluation. - Strong programming and software engineering skills; experience building reliable production systems. - Experience with modern deep learning frameworks such as **PyTorch** or **TensorFlow**. - Experience diagnosing and solving issues involving data/feature quality, model quality, training, or serving performance. --- **Preferred Qualifications** - Experience developing user signals, features, or learned user representa
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