- Spotify
- London, Greater London
- Full-Time
- 6 days ago
Machine Learning Engineer - Subscriptions.
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Machine Learning Engineer - Subscriptions: our view in 3 lines...
- The Role:This role is for an engineer building production machine learning capabilities that personalize the subscriber journey from conversion through long-term retention.
- The Person:The person will lead ML initiatives from technical specification through deployment and impact measurement, maintain reliable and observable ML systems, and develop scalable models for subscriber personalization and retention.
- Requirements:The ideal candidate has experience building production ML systems, strong machine learning theory and practice, Python or Scala, PyTorch, data pipelines, and preferably GCP or AWS experience.
About the role
At Spotify, our Subscriptions Mission focuses on creating experiences that help listeners discover the value of Spotify, become subscribers, and stay with us for the long term. By combining technology, experimentation, and a deep understanding of our users, we help connect artists and fans while building a sustainable subscription business.
Within the Subscriptions Mission, the Subs Foundational Models Product Area is part of the Subs User Understanding Studio. We build scalable machine learning solutions that personalize the subscriber journey, from conversion through to long-term retention. Working closely with teams across the mission, we develop ML solutions that improve key business metrics, including subscriber growth, monthly active users, and Premium retention.
We're looking for a Machine Learning Engineer to join our Radius squad. You'll take ownership of impactful ML initiatives, develop and improve production models, and contribute to the foundational ML capabilities that power personalization and optimization across Subscriptions.
Your work could include reducing friction for Free users while balancing retention and conversion, improving payment experiences to reduce Premium churn, or developing reusable models such as subscriber embeddings that support multiple products and use cases.
What You'll Do
Who You Are
Where You'll Be
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We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location
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This team operates within the Central European and GMT time zone for collaboration.

