Software Engineer, Machine Learning

AppLovin•Palo Alto, CA

2h•$150,000 - $224,000

About The Position

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. 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.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.
4+ years of experience developing and deploying machine learning systems in production environments.
Experience with machine learning or deep learning in areas such as recommendation, ranking, retrieval, prediction, advertising, representation learning, or related applications.
Experience developing and training machine learning models using large-scale datasets.
Strong understanding of machine learning fundamentals, including model architectures, optimization, representation learning, feature engineering, and model evaluation.
Strong programming and software engineering skills, with experience building reliable production systems.
Experience with modern deep learning frameworks such as PyTorch or TensorFlow.
Experience diagnosing and solving problems involving data and feature quality, model quality, training, or serving performance.

Nice To Haves

Experience developing user signals, features, or learned user representations for large-scale machine learning systems.
Experience with large-scale recommendation or advertising systems, including candidate generation, retrieval, ranking, or prediction.
Experience with representation learning, embeddings, feature interaction, or multi-task learning using large-scale user signals.
Experience measuring the incremental value of user signals and understanding their downstream impact on ranking or recommendation performance.
Experience developing and scaling deep learning architectures for recommendation, ranking, or advertising applications.
Experience with distributed model training and large-scale ML infrastructure.
Experience optimizing training or inference workloads on GPUs or other accelerators.
Experience optimizing ML systems for latency, throughput, memory utilization, or computational efficiency.
Experience designing and analyzing online experiments and offline model evaluations.

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.
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.