ML Data Infrastructure Engineer

The role, as AppLovin Corporation describes it

About AppLovin AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end advertising solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com .

To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others.

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 provides a competitive total compensation package with a pay for performance rewards approach. Total compensation at AppLovin is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Depending on the position offered, equity, and other forms of incentive compensation (as applicable) may be provided as part of a total compensation package, in addition to dental, vision, and other benefits.

Other Types of Pay: Equity eligible

Health Insurance: Medical, Dental, Vision, Life, Disability

Retirement Benefits: 401(k) Retirement Plan

Paid Time Off: Unlimited Discretionary Time Off

Paid Holidays: 10 paid holidays per year

Paid Sick Leave: 80 hours per year

Method of Application: Apply online

Application Window: The application window is expected to close within 30 days of the posting date.

All questions or concerns about this posting should be directed to [email protected].

CA Base Pay Range $221,000 — $331,000 USD

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 .

If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send us a request at [email protected]

AppLovin will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. If you’re applying for a position

Read the full advert and apply on AppLovin Corporation’s site →