ML Infrastructure Engineer

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.

A Day in the Life

As 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.

The Impact You'll Make

• Design, 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

Who You Are

• Have 0-2 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

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.

CA Base Pay Range

$124,000—$250,000 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 jobs@applovin.com

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 in California, learn more here.

To support an efficient and fair hiring process, we may use technology-assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers.

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