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|Job Category :||Entertainment Industry|
|Position Name:||Software Development Engineer, Podcast Recommendations Team, Commerce Science – Apple Media Products|
|Location :||New York, NY|
|Job Description :||Summary
Posted: Dec 3, 2021
Role Number: 200318140
The AMP Engineering team is one of the most exciting examples of Apple’s long-held passion for combining art and technology. We are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books. We do it on an extensive scale, meeting Apple’s high expectations with dedication to deliver a huge variety of entertainment in over 35 languages to more than 150 countries. We build secure, end-to-end solutions and 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 extraordinary software engineers with deep experience in large scale data processing systems and highly scalable applications and web services. If you have a passion to push the limits of distributed computing frameworks to get every ounce of performance out of them, comfortable working with Object Oriented programming techniques and have familiarity with modern server technologies (e.g., non-blocking IO, distributed storage etc.) look no further. This team exhibits strong attention to detail, have excellent analytical capabilities and have passion for development – we would love to have you join us!
The candidate will be able to demonstrate a strong practical understanding of how to develop practical, fault-tolerant high-performance distributed systems and data pipelines at scale.
Applicants will have 3+ years experience in industry or research developing robust, server-side Java, Scala code and will be able to demonstrate creativity, initiative and the ability to work to deadlines.
Java, Scala or equivalent Object Oriented Languages
Experience with Map/Reduce, Spark is preferred
Understanding of HTTP and restful API design
Experience with Machine Learning and Big Data pipelines
Knowledge on Swift or Objective-C is nice to have
We’re a diverse collective of thinkers and doers, continually reimagining what’s possible to help us all find what we love in new ways. Our engineers have created some tough acts to follow, and they continue to lead us to innovative breakthroughs. Because they’re driven not by what would be easy, but by what would be amazing. The AMP Commerce Optimizations team powers decisioning engines across App Store, iTunes and other commerce flows to provide our customers with a seamless purchase experience. We are looking for extraordinary researchers to help build next-generation commerce and payment features for Apple’s ground breaking devices and platforms. IN THIS ROLE YOU WILL: – Architect, prototype and collaborate to deliver highly efficient fault tolerant scalable/runtime systems. – Design and develop run-time services and off-line data pipelines in Java. – Build off-line pipelines, data analysis and A/B experiment analysis in Scala/Spark. – Collaborate with the Research team to build, train and ship recommendation models using Machine Learning. – Design, build and test debugging tools to help visualize recommendation models and analyze software problems. – Build prototypes, A/B Test pipelines to evaluate algorithm improvements. – Develop pipelines to process and store big data (both content and user) on Hadoop using Scala/Spark. – Great team player with the ability to communicate technical concepts effectively to others.
Education & Experience
Master’s degree or foreign equivalent in Computer Science, Information Technology, Software Engineering, or related field and 2 years of experience in the job offered or related occupation. Apple is an Equal Opportunity Employer that is committed to inclusion and diversity. We also take affirmative action to offer employment and advancement opportunities to all applicants, including minorities, women, protected veterans, and individuals with disabilities. Apple will not discriminate or retaliate against applicants who inquire about, disclose, or discuss their compensation or that of other applicants.
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