Machine Learning Engineer - Subscriptions
Spotify · London, London
Machine Learning Engineer - Subscriptions at Spotify, based in London, London. This is a permanent role.
- Salary
- Competitive
- Location
- London, London
- Contract
- Permanent
- Posted
- 6 hours ago
- Closes
- 23 Nov 2026
- Sector
- Data Analyst
Reference 19c12e29-b6d4-4d97-9053-48238fb7c381
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.
Reference: 19c12e29-b6d4-4d97-9053-48238fb7c381 · Posted 6 hours ago · Closes 23 Nov 2026 · Listed via Spotify
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