Software Engineer, Machine Learning

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: .

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.

• 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.

• Bachelors 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.

• 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.