Software Engineer, Machine Learning

Details

AppLovin is seeking a Software Engineer with strong machine learning expertise to advance user signal and recommendation technologies across its advertising platform, which reaches more than 1 billion users globally. The role covers large-scale machine learning problems involving user signals, representation learning, ranking, retrieval, model architecture, and optimization.

Responsibilities

• Develop and improve user signals, features, and representations used by large-scale machine learning models for advertising and recommendation.
• Explore machine learning approaches for learning 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 for 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.
• Solve machine learning problems involving 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 in 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 evaluate the incremental value of user signals and model improvements and their impact on product and business outcomes.
• Collaborate with engineering, data, and product teams to bring new user signals and machine learning approaches from experimentation into production.

Requirements

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 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, including 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 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 machine learning infrastructure.
• Experience optimizing training or inference workloads on GPUs or other accelerators.
• Experience optimizing machine learning systems for latency, throughput, memory utilization, or computational efficiency.
• Experience designing and analyzing online experiments and offline model evaluations.

Compensation and Benefits

• CA base pay range: $150,000–$224,000 USD per year.
• Competitive total compensation package with a pay-for-performance rewards approach.
• Equity eligibility.
• Medical, dental, vision, life, and disability insurance.
• 401(k) retirement plan.
• Unlimited discretionary time off.
• 10 paid holidays per year.
• 80 hours of paid sick leave.

Application Information

• Method of application: Apply online.
• The application window is expected to close within 30 days of the posting date.
• Questions or concerns about the posting can be directed to [email protected].

AppLovin is an equal opportunity employer committed to inclusion and diversity. The company will consider reasonable accommodation requests and will consider applicants with criminal histories in accordance with applicable law. Technology-assisted tools, including artificial intelligence, may be used to support the hiring process, but all hiring decisions are ultimately made by human reviewers.