ML Data Platform Engineer

Applovin Corp.; Palo Alto, Northern (CA, KY); Hybrid; USD 150,000 - 224,000; Full time

Benefits offered by this job

Equity eligible

Unlimited Discretionary Time Off

10 paid holidays per year

80 hours sick leave

Job summary

AppLovin is building a high-performance ML data platform to support model training and feature serving across global teams. You will design scalable data processing infrastructure and collaborate with researchers to implement new architectures that enable fast experimentation and reliable deployment.

The role emphasizes strong software engineering, distributed systems fundamentals, and attention to data quality and performance as you help optimize pipelines from ingestion to feature delivery.

Qualifications

• Have 1 - 3 years of experience and a minimum of a BS and/or MS in Computer Science.
• Strong software engineering fundamentals, with experience building high-throughput, fault-tolerant distributed systems.
• Hands-on experience with distributed computing frameworks such as Apache Spark or Flink.
• Solid grounding in data structures, systems design, and performance optimization.
• Strong problem-solving skills and attention to detail.

Responsibilities

• Design and build data processing infrastructure for model training and feature serving, optimizing for performance, reproducibility, and traceability
• Collaborate closely with research teams to design and implement novel data processing architectures for emerging model and training paradigms
• Identify and resolve performance bottlenecks across the training data pipeline, from raw data ingestion to feature delivery
• Establish best practices, tooling for data infrastructure used across ML teams

Skills

Distributed systems

Software engineering

Data structures

Performance optimization

Problem solving

Education

BS/MS in Computer Science

Tools

Apache Spark

Flink

Job description

AppLovin is building a high-performance ML data platform to support model training and feature serving across global teams. You will design scalable data processing infrastructure and collaborate with researchers to implement new architectures that enable fast experimentation and reliable deployment.

The role emphasizes strong software engineering, distributed systems fundamentals, and attention to data quality and performance as you help optimize pipelines from ingestion to feature delivery.