ML Data Infra Engineer: Scalable Pipelines & Training

AppLovin; Palo Alto (CA); On-site; USD 150,000 - 224,000; Full time

Benefits offered by this job

Health insurance

401(k) retirement plan

Unlimited discretionary time off

10 paid holidays per year

80 hours paid sick leave

Job summary

AppLovin is a leading provider of end-to-end advertising technology solutions. We are seeking an experienced engineer to design and build data processing infrastructure for model training and feature serving in a high-scale environment.

You will collaborate with research teams to develop novel data processing architectures for evolving ML paradigms and optimize the data pipeline from ingestion to feature delivery.

Qualifications

• 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

Spark

Flink

Data structures

Performance optimization

Problem solving

Job description

AppLovin is a leading provider of end-to-end advertising technology solutions. We are seeking an experienced engineer to design and build data processing infrastructure for model training and feature serving in a high-scale environment.

You will collaborate with research teams to develop novel data processing architectures for evolving ML paradigms and optimize the data pipeline from ingestion to feature delivery.