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    Machine Learning Engineer, Personalization

    Posted 19 hours ago

    Job Description

    The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.

    Samba sits at the heart of Spotify’s personalization engine, powering experiences like autoplay, radio, and personalized mixes. We work on complex sequencing and optimization problems—balancing what users love with how Spotify supports creators and the business.

    Our team blends machine learning, backend engineering, and data expertise, and collaborates across North America and Europe to deliver impactful, real-time personalization at scale.

    What You'll Do

  • Design and build machine learning systems that optimize ranking and sequencing across personalized surfaces
  • Develop multi-objective optimization strategies that balance user satisfaction with business outcomes
  • Collaborate closely with cross-functional partners including product, data science, and engineering teams to align on goals, share context, and deliver impactful solutions
  • Work across ML, backend, and data layers to bring models into production
  • Contribute to scalable infrastructure supporting high-volume user interactions
  • Run experiments and use insights to continuously improve performance
  • Help shape technical direction and raise the bar for engineering excellence within the team
    • ,

    Who You Are

  • You have 5+ years of experience in machine learning, data, or backend engineering
  • You are experienced with production-grade systems and scalable architectures
  • You have worked on recommendation systems, ranking, or optimization problems
  • You bring a T-shaped skillset across ML, data, and backend domains
  • You are comfortable navigating ambiguity and solving complex problems
  • You care about user experience and measurable impact
  • You enjoy collaborating across disciplines and geographies
    • ,

    Where You'll Be

  • This role is based in London or Stockholm
  • We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.
    • 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.

    Please mention that you found this job on MoAIJobs, this helps us grow. Thank you!

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    Spotify

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    About the job

    Posted on

    Apr 15, 2026

    Apply before

    May 15, 2026

    Job typeFull-time
    CategoryML Engineer
    Location
    RemoteLondonStockholm

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