Machine Learning Scientist - Ad Platforms at Apple (Cupertino, CA)

At Apple, we work every day to create products that enrich peoples lives. Our Advertising Platforms group makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work. Today, our technology and services power advertising in Search Ads in the App Store and Apple News. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy. We are looking for an aspiring and ambitious individual who can thrive in a fast paced agile environment and work at the leading of ML and privacy frameworks. Our group is developing next generation of Ad Tech technology with a strong focus on user privacy and in the process it is redefining the industry. For end-users, we aim to provide high privacy guarantees and for advertisers we want to enable new mechanisms to build effective campaigns. This position involves working on large volumes of data, identifying meaningful data patterns, assuring the integrity and breadth of the data, measuring user, campaign and app performance, and finally analyzing the results of extremely large-scale experiments. The role involves using state of the art language models, collaborative filters and dense representations. In addition, the successful candidate will also apply advanced ML techniques for federated learning where privacy mechanisms are safeguarded at the very onset and delightful relevance experiences are built by applying encryption techniques, on-device segmentation, advanced language models, ranking algorithms by utilizing the best of aggregated server and on-device data.

You will have the opportunity to work on a platform with extreme scale and performance requirements. You would be applying your skills to work across the stack to develop, test, deploy and maintain ML based software solutions. Develop machine learning models using relevant frameworks such as TensorFlow and PyTorch. Implement and adapt deep learning network architectures, such as CNN and RNN. You would participate in cutting edge research in artificial intelligence and machine learning applications.

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