Software Engineer, Machine Learning

About the role

About AppLovin

AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end advertising solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com.

To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others.

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.

Responsibilities

- 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.