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.

The Personalization (PZN) team is at the heart of how Spotify connects listeners with the content they love. Every day, hundreds of millions of people rely on the experiences we build, from Home and Search to Made For You and Discover Weekly.

Surfaces-NPV is an EU-based squad within the Recommendation Surfaces product area. We own recommendation quality on the Now Playing View, one of Spotify’s most personal and high-impact surfaces, and drive the introduction of new content verticals. We’re a small, senior-heavy team that values craft, autonomy, and shipping. We use AI coding tools such as Claude Code, experiment constantly, and believe the best ML engineers understand the full stack from user need to production system.

What You'll Do:
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Design, train, and ship machine learning models that power recommendations on the Now Playing View for hundreds of millions of users

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Own ranking systems end-to-end, from experimentation and training pipelines to online serving and monitoring

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Build and iterate on generative and agentic ML approaches to improve session steering and cross-content discovery

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Work in an AI-native development environment, using AI tools to accelerate development while applying strong engineering judgment

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Run A/B experiments, define success metrics, and translate improvements into measurable user impact

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Collaborate closely with engineers, data scientists, researchers, and product managers to bring ideas into production

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Shape the ML roadmap by identifying high-impact opportunities and mentor teammates

Who You Are:
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You have hands-on experience building recommendation or personalization systems at scale

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You’re comfortable working across the ML stack, including pipelines, backend systems, and infrastructure

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You think in products and understand how model decisions impact user experience

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You’re fluent with AI-assisted development and use it to accelerate experimentation thoughtfully

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You’re curious about emerging approaches like generative models and agentic ML systems

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You’ve taken models from prototype to production and care about reliability and monitoring

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You’re comfortable with ambiguity and enjoy defining new approaches in evolving problem spaces

Where You'll Be:
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This role is based in London

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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.

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.

Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.