Senior Machine Learning Engineer, GenRecs, Personalization Job at Spotify, New York, NY

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  • Spotify
  • New York, NY

Job Description

The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix. 

Where You’ll Be (choose what is best based on the role / team!) We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location. This team operates within the Eastern Standard time zone for collaboration.

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What You'll Do
  • Be a technical leader within the team you work with and within Spotify in general
  • Coordinate technical projects across teams within Spotify
  • Facilitate collaboration with other engineers, product owners, and designers to solve interesting and challenging problems for delivering various media worldwide
  • Be a valued member of an autonomous, cross-functional agile team
  • Architect, design, develop, and deploy ML models 
  • Be a leader in Spotify’s ML community and work collaboratively and efficiently on existing platforms and systems.

Who You Are
  • You have experience being a technical leader or mentorExperience training, improving and operating internet-scale ML models
  • You have a strong background in machine learning, especially experience with recommender systems
  • Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams
  • You have experience in designing and building ML systems
  • You are experienced with feature engineering and building scalable data pipelines
  • You have a deep understanding of ML systems and infrastructure
  • You have experience in Tensorflow or PyTorch. Experience with Kubeflow, Ray is a plus
  • Experience with generative models is a plus

Where You'll Be
  • We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location
  • This team operates within the Eastern Standard time zone for collaboration

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The United States base range for this position is $176,166 $251,666 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.

Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.

Job Tags

Remote job, Full time, Worldwide, Flexible hours,

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