Machine Learning Engineer, Personalization Specialist

7 days ago


Étreux, Hauts-de-France, United States Spotify Full time

At Spotify, we're behind some of the world's most-loved music features. As a Machine Learning Engineer, you'll be part of the team that makes deciding what to play next easier and more enjoyable for every listener.

Key Responsibilities
  • Develop ML models used in generating candidates for music recommendations on Spotify's Home surface, including the Main feed and music subfeed.
  • Create ML models to order music content appearing on carousel shelves on Spotify.
  • Collaborate with a cross-functional agile team to build new user experiences that advance our mission to connect artists and fans in personalized and relevant ways.
  • Prototype new approaches and productionize solutions at scale for hundreds of millions of active users.
  • A/B test the ML models you've developed to understand how they improve the recommendations experience for users.
Requirements
  • Strong background in machine learning with expertise in personalized algorithms, especially recommender systems.
  • Hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages.
  • Experience with data pipeline tools like Apache Beam or Scio and cloud platforms like GCP or AWS.
  • Care about agile software processes, data-driven development, reliability, and disciplined experimentation.
  • Love customers and care about their experience.
Salary and Benefits

The estimated salary range for this position is $171,903.00 - $245,575.00 per year, plus equity. We also offer a comprehensive benefits package, including health insurance, six months paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, and 13 paid flexible holidays.

About Us

We are a distributed workforce enabling our team members to find a work mode that suits them best. For this role, it can be within the Americas region, with options for remote work. Our core hours are aligned with the Eastern Standard time zone for collaboration.



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