Podcasting was born on iPod, and this team’s work has led the category ever since. Today, podcasts are a worldwide phenomenon, and these engineers continue to evolve the Apple Podcasts app and ecosystem across all our devices. And for podcast creators, networks, and advertisers, the team created Podcast Analytics, which gives insights into completion rates and subscription data for their listeners worldwide.
A strong machine learning focus helps the team improve search and recommendations, so listeners can easily discover podcasts they’ll love from well over a half million offered. As podcast fans themselves, these engineers love shaping a medium that’s just begun to show its potential to entertain, inform, and inspire. That passion is just one reason why Apple Podcasts remains the world’s most popular podcast app, giving this small team a huge influence on the industry. Join them and make some impact of your own.
Areas of work include macOS/iOS Engineering, Full-Stack Engineering, Front-End Engineering, Back-End Engineering, Quality Engineering, Machine Learning Engineering, Data Science, Data Engineering, Site Reliability Engineering, and Engineering Project Management.
The Apple Media Products Data Engineering team is looking for great engineers. We are looking for the very best people to build and positively improve features and services driving the iTunes Store, App Store and Apple Music. Our team is responsible for many of the key systems powering personalisation features of the AMP ecosystem. This includes features such as ratings & reviews, purchase history, sync services and many more. Here you have a phenomenal opportunity to help build and evolve global-scale, leading-edge dynamic data systems, with positions currently available as we grow our amazing London team. Our work covers the full stack, from: internet-facing web services, internal services using various flavours of RPC; design and implementation of data pipelines/life-cycles (Kafka); Hadoop infrastructure, strategy and implementation; distributed key-value storage (Voldemort, Cassandra, Redis, etc); and putting all this together to operate live customer-facing features with millisecond-latencies across multiple data centres with petabyte datasets and > 2 billion users. We promote innovation and new technology to further improve our creative output. If you’re an all round and performance-savvy Java server engineer with an interest in, and experience of, large scale Data technologies and systems at an unprecedented scale we’d love to hear from you.
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