🕒 4 days ago
Build and deploy production-grade machine learning systems for client challenges, shaping scalable software architecture and deployment standards while advising stakeholders and collaborating across technical and commercial teams.
Machine Learning Engineer - Subscriptions
🕒 3 days ago
Machine LearningProduction ML SystemsPytorchPython
📜 Description
- Lead ML initiatives end to end, from translating business needs into technical specifications to shipping models and measuring subscriber impact.
- Own ML system components and make technical trade-offs while maintaining observability, reliability, and documentation.
- Collaborate with research, design, data science, product, and engineering partners on subscription features.
- Prototype approaches and productionize solutions at scale for hundreds of millions of active users.
- Improve model quality through optimization, testing, and tooling; share context to help teammates work effectively.
- Use AI-assisted engineering to reduce repetitive work while owning the quality of shipped solutions.
🛠️ Requirements
- You have experience building and shipping production ML systems
- You have a strong background in machine learning, theory, and practice
- You are comfortable explaining the intuition and assumptions behind ML concepts
- You can own well-scoped ML projects end to end and make sound technical decisions independently
- You communicate clearly with stakeholders and can explain the reasoning behind your technical choices
- You are curious, open to feedback, and keen to grow your scope over time
- You have hands-on experience implementing and maintaining production ML systems in Python, Scala, or similar languages, including model development with PyTorch
- You are experienced with building data pipelines, and you are self-sufficient in getting the data you need to build and evaluate your models
- You preferably have experience with cloud platforms like GCP or AWS
- You care about agile software processes, data development, reliability, and focused experimentation
Full job description
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.
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