AppLovin fuels the growth of many of the world's most popular mobile games and game studios. Since 2012, the company’s technologies have been instrumental in driving the explosive growth of games from its studio partners and its own studios. AppLovin makes those same technologies available to all game developers, resulting in a richer mobile game ecosystem and better games for people everywhere. AppLovin is headquartered in Palo Alto, California with several offices globally. Learn more at applovin.com.

AppLovin is one of Inc.‘s Best Workplaces and a recipient of the 2019 Glassdoor Top CEO employee’s choice award. The San Francisco Business Times’ awarded AppLovin one of the Bay Area’s Best Places to Work and the Workplace Wellness Award which recognizes businesses that are leaders in improving worker well-being.

AppLovin is expanding its fresh R&D center in Israel.   If you’re looking to work with the best technologists in the world, impact, learn and enjoy every minute of it - send us your CV and we would love to meet you. The ideal candidate is super passionate about deep, innovative technology, independent, self- motivated, results-oriented and sharp.

Job Requirements:

      • At least 5 years industry experience with backend development Python experience 
      • Successfully completed at least one basic machine learning academic/online course 
      • At least 3 years industry experience working with cloud unix machines Networking experience 
      • Four-year college degree from a top university, preferably in Computer Science or similar Experience working with global teams 
      • Proficient in English 
Nice to Have:

      • MSc or PhD degree in a quantitative discipline 
      • Machine learning research experience 
      • Computer vision research experience 
      • Applied experience with machine learning on large datasets Industry cloud devops experience Industry experience as a software architect 

Job Responsibilities:

    • End-to-end responsibility of the machine learning server side stack 
    • Train, benchmark and deploy machine learning models Monitor and maintain machine learning production pipeline 
    • Design and implement a training and benchmarking machine learning framework 
    • Design and implement internal tools for data labeling 
    • Manage server instances deployed on a global cloud and colocation facilities 
    • Participate in system architecture and prioritization discussions 

 

 

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