Spotify

Spotify

Machine Learning Engineer - Personalization

New York, New York, USA · full-time

Sponsorship not specified$170k-$212kDetected 49 days ago
PythonJavaScalaGCPCloud PlatformsMachine LearningSparkNLPLLMsMLOpsStatisticsA/B Testing

About the role

  • 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.
  • These can include new content types in our Search engine or emerging user interaction patterns.

Responsibilities

  • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses.
  • 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.
  • Our team's mission is to bring emerging search and agentic experiences to a mature state: exploring, defining, building, validating and optimizing new ideas.

Requirements

  • 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.

Compensation

  • $170k-$212k

Benefits

  • The United States base range for this position is $170,000 - $212,000 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.

Company info

  • Our platform is for everyone, and so is our workplace.
  • At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone.

Equal opportunity

  • Spotify is an equal opportunity employer.
  • equal opportunity employer.
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This listing is sourced directly from Spotify's careers page and normalized into a canonical job model.