Machine Learning Engineer - Subscriptions at spotify

<div> <p data-start="291" data-end="613">The Subscriptions Mission team builds and evolves Spotify’s subscription products to drive growth, retention, and user value. We focus on creating seamless, personalized experiences that help listeners discover the right plan and get the most out of Spotify, while enabling sustainable business growth.</p> <p data-start="615" data-end="1086">You’ll join the User Understanding space within the Subscriptions Mission, where we build machine learning systems that personalize the subscription journey for millions of listeners. Sitting in the Lorax squad, this team focuses on optimizing the full subscription funnel—from discovery to conversion—by delivering relevant, timely, and meaningful experiences. Our work directly impacts how users engage with Spotify and how we grow our subscriber base.</p> </div> What You'll Do • Contribute to designing, building, evaluating, and improving machine learning models that power personalization across the subscription funnel • Work closely with a cross-functional team of engineers, data scientists, product managers, designers, and researchers to ship impactful features • Prototype new machine learning approaches and scale them to production for hundreds of millions of users • Help optimize experimentation frameworks, testing strategies, and tooling to improve model quality and reliability • Build and maintain robust data pipelines and production-ready ML systems • Participate in knowledge sharing within the machine learning community across Spotify • Contribute to improving how we personalize messaging, offers, and user journeys across discovery and conversion surfaces Who You Are • You have 3+ years of experience applying machine learning in production environments • You are comfortable explaining machine learning concepts, assumptions, and trade-offs to both technical and non-technical partners • You have hands-on experience building and maintaining production ML systems using Python, Scala, or similar languages • You have experience working with modern ML frameworks such as PyTorch or distributed systems like Ray • You are experienced in building data pipelines and independently sourcing and preparing data for modeling • You have worked with cloud platforms such as GCP or AWS • You care about experimentation, iteration, and using data to guide decisions • You enjoy working in collaborative, cross-functional teams and contributing to shared outcomes • You are motivated by driving measurable business impact through your work Where You'll Be • This role is based in New York or Boston • 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.