Senior 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.

Growlithe is a fast-moving squad within Spotify's Conversation Product Area, focused on bringing emerging Search and agentic experiences to life. We explore, define, build, validate, and optimize new ideas to help listeners discover the content they love. Our work spans personalized Search, music video discovery and ranking, audiobook Search expansion, and the foundations of more social Search experiences.

We're looking for a Senior Machine Learning Engineer to help shape the next generation of Search at Spotify. You'll work at the intersection of machine learning, personalization, and product development, turning complex challenges into intuitive discovery experiences for millions of listeners.

You have experience building, deploying, and improving machine learning systems in production environments.

You have a strong understanding of machine learning approaches for information retrieval, search ranking, recommendation systems, or personalization.

You are proficient in Python and comfortable working with modern machine learning frameworks and data processing tools.

You have experience and a desire to lean into backend engineering at Spotify scale.

You understand how to design and evaluate experiments, interpret model performance, and translate technical findings into product improvements.

You have experience working with large-scale datasets and understand the challenges of building reliable, scalable machine learning systems.

You are curious about emerging AI technologies, including large language models and agentic systems, and interested in their potential to improve discovery experiences.

You enjoy working across disciplines, communicating technical ideas clearly, and learning from different perspectives.

You are comfortable navigating ambiguity, taking ownership of complex challenges, and balancing experimentation with practical delivery.

This role is based in Eastern Standard Time

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.