Description
The Personalization team at Spotify makes deciding what to play next easier and more enjoyable for every listener. We're behind some of Spotify's most-loved features, including Blend and Discover Weekly. Our team works at the intersection of machine learning, music understanding, and user experience. We focus on generating music sessions that power experiences like conversational playlist generation, giving users more adaptive and intuitive control over what they listen to.
As a Machine Learning Engineer on our team, you'll design, build, evaluate, and ship LLM-based solutions that give users more adaptive control over their listening experience. You'll work on prompted playlist experiences with a focus on music fulfillment and session generation. You'll collaborate with cross-functional partners across user research, design, data science, product, and engineering. You'll prototype new ML approaches and bring them into production at global scale. You'll build and improve systems that connect artists and fans in personalized and meaningful ways. You'll contribute to the development of scalable ML systems serving hundreds of millions of users. You'll promote best practices in ML system design, testing, evaluation, and deployment across the organization. You'll actively contribute to a strong community of machine learning practitioners at Spotify.
We're looking for experienced machine learning engineers who enjoy solving complex real-world problems in collaborative environments. You should have a strong background in machine learning, natural language processing, and generative AI. You should be comfortable applying theory to build real-world, production-ready applications. You should have hands-on experience building and deploying end-to-end ML systems at scale. You should be familiar with LLM-based systems and techniques for improving them using human feedback such as reinforcement fine-tuning, DPO, or similar approaches. You should have experience designing modular ML architectures and writing technical specifications in partnership with product teams. You should be experienced with large-scale distributed data processing tools such as Apache Beam or Apache Spark. You should have worked with cloud platforms like GCP or AWS.