About this role
Designing and building robust data processing infrastructure for model training and feature serving takes up the bulk of the day-to-day work in this position. The ML Data Platform team focuses on constructing a high-performance, globally distributed ecosystem of services that serves as the foundation for rapid system development across the organization.
Joining this group means tackling complex technical challenges like upgrading software infrastructure and collaborating with researchers on novel data processing architectures. The person in this seat will identify bottlenecks across pipelines from raw ingestion to feature delivery while establishing best practices for machine learning tooling. Engineers who thrive here typically bring strong distributed systems fundamentals and an interest in optimizing large-scale data workflows.
What you will do
- Design and build data processing infrastructure for model training and feature serving while 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, spanning from raw data ingestion to feature delivery.
- Establish best practices and develop tooling for data infrastructure used across multiple machine learning teams.
What you need
- Candidates must hold a minimum of a BS and/or MS in Computer Science along with 1 to 3 years of professional experience.
- Demonstrate strong software engineering fundamentals with previous experience building high-throughput and fault-tolerant distributed systems.
- Possess hands-on experience working with distributed computing frameworks such as Apache Spark or Flink.
- Maintain a solid grounding in core data structures, systems design principles, and performance optimization techniques.
- Exhibit strong problem-solving skills and meticulous attention to detail.
Nice to have
- Background in MLOps, Data Infrastructure, or ML Infrastructure
- Experience with ML training pipelines, feature stores, or model-serving systems
Benefits
- Equity eligible
- Medical, Dental, Vision, Life, Disability Health Insurance
- 401(k) Retirement Plan
- Unlimited Discretionary Time Off
- 10 paid holidays per year
- 80 hours of paid sick leave per year
Skills and tech stack
Questions candidates ask
What is the salary range for this role?
The California base pay range for this position is $221,000 to $331,000 USD. Total compensation is based on factors including market location, job-related knowledge, skills, and experience.
What kind of total compensation package is provided?
AppLovin provides a competitive total compensation package with a pay for performance rewards approach. Depending on the position offered, equity and other forms of incentive compensation may be included alongside benefits.
How long is the application window open?
The application window is expected to close within 30 days of the posting date. All questions or concerns about the posting should be directed to peopleops@applovin.com.