Machine Learning Engineer - Personalization, Horizon at spotify

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. You’ll join the Horizon Product Area within the Sessions Studio, part of Spotify’s Personalization Mission. This team focuses on inventing and evolving new listening experiences powered by emerging technologies. From AI DJ to promptable playlists and generated podcasts, we’re exploring how agentic systems and generative AI can reshape how people interact with audio. You’ll work at the intersection of product innovation and cutting-edge machine learning to bring entirely new experiences to life for millions of listeners. What You'll Do • Design, build, evaluate, and ship agentic based features and interactive experiences to bring our products to the next level • Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways • Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users • Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization • Actively contributed to a strong community of machine learning practitioners at Spotify Who You Are • An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment • Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications • Hands-on expertise with implementing end-to-end production ML systems at scale. Experience with production LLM scale based systems is a plus • Experience with incorporating human feedback to improve LLM based systems using technicals like DPO, KTO, and reinforcement fine-tuning • Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams • Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS Where You'll Be • We offer you the flexibility to work where you work best! For this role, you can be within the European region as long as we have a work location. • This team operates within the GMT/CET time zone for collaboration. • Excluding France due to on-call restrictions. 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.