ML Data Infrastructure Engineer

AppLovin provides end-to-end advertising technologies that help businesses connect with, monetize, and grow their global audiences. The ML Data Infrastructure Engineer will build and optimize data processing infrastructure for model training and feature serving, collaborate with research teams on new data architectures, and establish tooling and best practices for ML data infrastructure.

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

Preferred

Background in MLOps, Data Infrastructure, or ML Infrastructure

Experience with ML training pipelines, feature stores, or model-serving systems

MLOps

Data Infrastructure

ML Infrastructure

ML Training Pipelines

Feature Stores

Model-Serving Systems

Benefits

Equity and other forms of incentive compensation may be provided as part of a total compensation package, depending on the position offered.

Medical, Dental, Vision, Life, Disability

401(k) Retirement Plan

Unlimited Discretionary Time Off

10 paid holidays per year

80 hours of paid sick leave per year

Company

Software and AI solutions for businesses to reach, monetize audiences.

Palo Alto, California, USA

https://www.applovin.com/

Founded in 2012

201-500 employees

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

Background in MLOps, Data Infrastructure, or ML Infrastructure

Experience with ML training pipelines, feature stores, or model-serving systems

Required skills

Represents the skills you have, click on the tags, Distributed Systems, Apache Spark, Apache Flink, Systems Design, MLOps, Data Infrastructure, ML Infrastructure, ML Training Pipelines, Feature Stores, Model Serving

Experience

1-3 years