Machine Learning Engineer, Personalization

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 We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.

This team operates within the Eastern Standard time zone for collaboration.