AppLovin ML Infrastructure Engineer II Edit Hide On-...

## About AppLovinAppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach, monetize and grow their global audiences. 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.## About the RoleAs a member of our software engineering infra 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. Our infra team is responsible for providing and maintaining scalable infrastructure with high throughput and low latency to our bidding ecosystem. You will be exposed to the whole pipeline of model delivery, including training, serving, and optimizations, etc.## QualificationsHave 2-4 years of experience and a minimum of a BS and/or MS in Computer Science. Have excellent knowledge of computer science fundamentals including data structures, algorithms, and coding. Good experience with C++, Python and/or Golang is a plus. Experience independently creating and maintaining projects.## ResponsibilitiesDesign, develop, and maintain large-scale distributed systems. Collaborate with various engineering teams to meet a wide range of technological challenges. Work closely with our research science team and backend team to contribute and influence the roadmap of our products and technologies. Influence and inspire team members. Speed up the performance of our online models. Optimize the model delivery pipeline. #J-18808-Ljbffr

Created: 2025-09-18