ML Data Infrastructure Engineer
AppLovin
CA$221,000 - CA$331,000 / year
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Description
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 peopleops@applovin.com.
Benefits
- health insurance
- 401(k)
- paid time off
- stock options
- sick leave
About this role
AppLovin's ML Data Infrastructure team is looking for an engineer to design and build the data processing backbone that powers the company's machine learning systems. You'd work on high-performance, globally distributed infrastructure for model training and feature serving, collaborating with research teams to optimize data pipelines from raw ingestion through feature delivery. The role involves identifying performance bottlenecks, establishing best practices across ML teams, and implementing novel data architectures as training paradigms evolve.
The position requires 1–3 years of experience and a CS degree, along with strong fundamentals in distributed systems design and hands-on work with frameworks like Apache Spark or Flink. You should be comfortable with performance optimization, data structures, and systems-level problem solving. Prior exposure to MLOps, feature stores, or model-serving systems is a plus but not required. This is an in-office role in Palo Alto with a base salary range of $221,000–$331,000, equity eligibility, and standard benefits including medical, dental, vision, 401(k), and unlimited discretionary time off.
How this employer is doing
solid
- H1B: Sponsors Visas
- Recent Acquisition
- Recently Raised Funding
- Sponsors Visas
In the news
Rep. Gilbert Ray Cisneros, Jr. Acquires Shares of AppLovin Corporation (NASDAQ:APP), The Lincolnian Online
This role's local market on JobMinglr
Pay for this role
$221,000 to $331,000 per year
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