AppLovin’s leading marketing software provides mobile app developers a powerful set of solutions to grow their mobile apps. AppLovin’s technology platform enables developers to market, monetize, analyze and publish their apps. The company’s first-party content includes over 200+ popular, engaging apps and its technology brings that content to millions of users around the world. AppLovin is headquartered in Palo Alto, California with several offices globally.
AppLovin is a Certified Great Place to Work, one of Inc.’s Best Workplaces and a recipient of the 2019 Glassdoor Top CEO employee’s choice award. The San Francisco Business Times and Silicon Valley Business Journal awarded AppLovin one of the Bay Area’s Best Places to Work in 2019, 2020 and 2021, and the Workplace Wellness Award in 2019 which recognizes businesses that are leaders in improving worker well-being.
Data driven decision-making is integral to marketing, game development and operations at Applovin. We’re looking for sharp, disciplined, and highly quantitative machine learning engineers with big data experience and a passion for digital marketing and game technologies to help drive informed decision-making. You will work with top-talent and cutting edge technology on, for example, but not limited to, performance marketing and next-generation games and have a unique opportunity to turn your insights into products influencing billions of users. The potential candidate will have an extensive background in distributed training frameworks, will have experience to deploy related machine learning models end to end, and will have some experience in data-driven decision making of machine learning infrastructure enhancement. This is your chance to leave your legacy and be part of a highly successful and growth company!What you'll be doing:
- Collaborate with colleagues across multiple teams (Data Science, Operation Engineering and Data Engineering) on unique machine learning system challenges at scale.
- Leverage distributed training systems to build scalable machine learning pipelines including ETL, model training and deployments in Real-Time Bidding space.
- Design and implement solutions to optimize distributed training execution in terms of computing resource (CPU, GPU) utilization, model training / inference latency and system-level bottlenecks.
- Research state-of-the-art machine learning infrastructures to improve data healthiness, model quality and state management during the lifecycle of ML models refresh.
- Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks.
- Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/ GPUs.
Your background and who you are:
- MS or Ph.D. in Computer Science, Software Engineering, Electrical Engineering or related fields.
- 3+ years of industry experience with Python in a programming intensive role.
- 3+ years of industry experience with distributed computing frameworks such as Hadoop/Spark, Kubernetes ecosystem, etc.1+ years of industry experience with very large-scale distributed training infrastructure.
- 1+ years of industry experience with popular deep learning frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch, Caffe, etc.1+ years of industry experience with major cloud computing services.
- 1+ years of experience with one or more of the following machine learning topics: classification, clustering, optimization, recommendation system, graph mining, deep learning.
- An effective communicator you shall be an ambassador of Applovin ML engineering at external forums and also have the ability to explain technical concepts to a non-technical audience.
Preferred Qualifications:
- Prior experience with ads product development (e.g., DSP/ad-exchange/SSP) and established a track record of innovation would be a big plus.
- Contributions to open source (e.g., C++/python/R packages) would be a plus.
- Strong C/C++ coding experience.
- Strong motivation to make downstream modelers’ work smoother.
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AppLovin is an equal opportunity employer and considers qualified applicants without regard to race, gender, sexual orientation, gender identity or expression, genetic information, national origin, age, disability, medical condition, religion, marital status or veteran status, or any other basis protected by law.