ML Data Infrastructure Engineer

As a member of our ML Data Platform team, you'll solve technical challenges, including upgrading and implementing state-of-the-art software infrastructure. The team builds a high-performance, high availability, globally distributed ecosystem platform of services that in turn provide the foundation for rapid development of novel new systems that integrate into that ecosystem and improve it.

The Impact You'll Make

• 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

Required 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

Preferred Qualifications

• Background in MLOps, Data Infrastructure, or ML Infrastructure
• Experience with ML training pipelines, feature stores, or model-serving systems

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.

 

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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.

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