AppLovin
ML Data Infrastructure Engineer
About this role
Join AppLovin's ML Data Platform team to design and build high-performance data infrastructure that powers machine learning workflows at scale. You'll optimize data pipelines for model training and feature serving while collaborating with research teams to implement cutting-edge data architectures.
What you'll do
- Design and build data processing infrastructure for ML model training and feature serving with focus on performance and reproducibility
- Collaborate with research teams to architect novel data processing systems for emerging ML paradigms
- Identify and resolve performance bottlenecks across the training data pipeline from ingestion to feature delivery
- Establish best practices and tooling standards for data infrastructure across ML teams
- Build and maintain high-throughput, fault-tolerant distributed systems serving global operations
- Work on ML training pipelines, feature stores, and model-serving systems
What they're looking for
- Distributed systems design and implementation
- Apache Spark or Flink
- Data structures and systems design
- Performance optimization and bottleneck analysis
- Software engineering fundamentals
- MLOps or ML infrastructure experience
- Feature store or model-serving systems
- Problem-solving and attention to detail
Benefits
- Competitive equity package
- Medical, dental, and vision insurance
- 401(k) retirement plan
- Unlimited discretionary time off
- 10 paid holidays plus 80 hours sick leave annually
- Disability and life insurance
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AppLovin
AppLovin builds a large-scale advertising platform that processes billions of requests daily through distributed systems and machine learning-powered bidding infrastructure. The company is hiring backend engineers, ML infrastructure engineers, and partner solutions engineers to develop and maintain high-performance systems, optimize bidding ecosystems, and support strategic advertising integrations.
- Website
- applovin.com
Likely interview questions
- Describe your experience building or optimizing distributed data pipelines at scale—what frameworks did you use and what performance improvements did you achieve?
- Walk us through how you've approached identifying and resolving a performance bottleneck in a high-throughput system.