ML Data Infrastructure Engineer
AppLovin, Palo Alto, CA
What you will do
- 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
What you need
- 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