The Apple Media Products Engineering team is one of the most exciting examples of Apples long-held passion for combining art and technology. These are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books. And they do it on a massive scale, meeting Apples high expectations with high performance to deliver a huge variety of entertainment in over 35 languages to more than 150 countries.
These engineers build secure, end-to-end solutions. They develop the custom software used to process all the creative work, the tools that providers use to deliver that media, all the server-side systems, and the APIs for many Apple services.
We are looking for a world-class Applied Researcher to build and enhance features improving discoverability of the content in iTunes Store, App Store, Music, Movies, Podcasts, and iBooks. If you are passionate about building phenomenal products and learning new technologies, this is the job for you. Come join our nimble teams and be part of impacting millions of customers.
Research, design and develop machine learning models for iTunes & App store recommendations. Propose, prototype and evaluate the algorithm improvements. Build personalized recommender systems for Apple Music, Apps & Games Recommendations, Video, Podcast and Books Recommendations using one or more of the following methods: Deep Learning, Matrix Factorization, Factorization Machines, Text Mining, NLP, Learn to Rank models etc. Build a pipeline for analyzing big data that consists of both content and user data on Hadoop using map/reduce techniques. Adapt machine learning algorithms to large scale data (big data). Develop and build cross validation for your models. Conduct human judgments and A/B experiments, improve ranking models based test data. Derive insights from the experimentation and convert them into feature improvements. Ship production quality code for the offline model building and work with engineering team to develop/deploy the run time system for the model. Analyze software performance problems and implement optimizations. Active contribution to identify areas of improvement in personalization and recommendation products. Ability to adapt latest in literature in the area to build efficient and scalable models.
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