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Sr. Machine Learning Engineer, Search Science - AI/ML Search/Recommendations/Personalization

Apple

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Job Summary


Job Type
Permanent

Seniority
Senior

Years of Experience
At least 10 years

Tech Stacks
Spark
iOS
Scala
Hadoop
Java

Job Description


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The Apple Media Products Engineering team is one of the most exciting examples of Apple’s 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 Apple’s 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.

Thanks to Apple’s unique integration of hardware, software, and services, engineers here partner to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apple’s privacy policy, one of Apple’s core values. Although services are a bigger part of Apple’s business than ever before, these teams remain small, nimble, and cross-functional, offering greater exposure to the array of opportunities here.

Key Qualifications
  • Deep understanding of the search and information retrieval fundamentals including indexing, query understanding, retrieval and ranking
  • Strong Industrial experience with one or more of the following: search, classification, regression, recommendation systems, targeting systems, ranking systems, fraud detection, online advertising, or related
  • 10+ years of relevant industry experience
  • Experience with Learning to Rank algorithms
  • Experience building big data pipelines with Hadoop, Java, Scala, and Spark
  • Familiarity with A/B experimentation and data/metric-driven product development
  • Passion. Our customers love what we do at Apple and we want the same from our engineers

Description
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 Search team powers search for iTunes, App Store, Apple Music, Apple TV, Podcasts, Books and more on iOS, macOS, tvOS, watchOS, web browsers, 3rd party devices, and Windows. We are looking for extraordinary researchers to help build next-generation search features for Apple's ground breaking devices and platforms. We are a key part of the Apple ecosystem!

You Will
- Think through complex research problems, simplify where necessary, invent when needed, to drive a principled vision from thought to reality
- Present key technical and novel research work in public forums
- Have a major impact on the way people search & discover on Apple devices worldwide.
- Be part of a team with strong expertise in software engineering, information retrieval, language processing, data mining, machine learning, scalable systems, and offline parallel processing (Hadoop, Scala, Spark).
- Be responsible for improving search recall and ranking.
- Use big data technology to evaluate and prioritize content discovery features.
- Conduct AB Tests to ensure we objectively measure improvements.
- Ensure successful deployment of features, code, data, and models to production.
- Collaborate with other world-class engineers, researchers, and statisticians to ensure that features and models are functioning at or above expected performance levels.
- Design & Implement solutions for automated unit and integration tests that enable continuous integration and delivery
- Support search on all devices (macOS, iOS, tvOS, watchOS, Siri/HomePod, and more) globally in languages from Arabic to Russian and everything in between

Education & Experience
MS in Computer Science or related discipline

Role Number: 200248439

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