ML Engineer – Large-Scale Ads & User Signals

AppLovin

Palo Alto, Northern (CA, KY)

Hybrid

USD 150,000 - 224,000

Full time

8 days ago

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Benefits offered by this job

Equity eligible

Unlimited Discretionary Time Off

10 paid holidays per year

CA base pay range

Job summary

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. You will work on large-scale ML problems, spanning signal learning, ranking, retrieval, and optimization, from feature development to production.

You will develop new representations, improve model quality and efficiency, and collaborate with engineering, data, and product teams to

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, or related 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.

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.