Job description

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. 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. Minimum Qualifications 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. Preferred Qualifications 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. AppLovin provides a competitive total compensation package with a pay for performance rewards approach. Total compensation at AppLovin is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Depending on the position offered, equity, and other forms of incentive compensation (as applicable) may be provided as part of a total compensation package, in addition to dental, vision, and other benefits. Other Types of Pay: Equity eligible Health Insurance: Medical, Dental, Vision, Life, Disability Retirement Benefits: 401(k) Retirement Plan Paid Time Off: Unlimited Discretionary Time Off Paid Holidays: 10 paid holidays per year Paid Sick Leave: 80 hours per year Method of Application: Apply online Application Window: The application window is expected to close within 30 days of the posting date. All questions or concerns about this posting should be directed to peopleops@applovin.com. CA Base Pay Range $221,000 — $331,000 USD AppLovin is proud to be an equal opportunity employer that is committed to inclusion and diversity. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or other legally protected characteristics. Learn more about EEO rights as an applicant here . If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send us a request at jobs@applovin.com AppLovin will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. If you’re applying for a position in California, learn more here . To support an efficient and fair hiring process, we may use technology-assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers. Please read our Global Applicant Privacy Notice to learn more about how AppLovin processes your personal information.

Requirements

Must-haves

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.

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.
Other Types of Pay: Equity eligible
Paid Holidays: 10 paid holidays per year
Paid Sick Leave: 80 hours per year

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlai

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off