Senior Machine Learning Engineer, Personalization, Muse

About The Role

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 a team working at the intersection of machine learning, music understanding, and user experience. We focus on generating music sessions powering experiences like systems that power conversational playlist generation to give users more adaptive and intuitive control over what they listen to. This team collaborates closely with product, design, user research, and data science to build personalized, high-impact features used by hundreds of millions of listeners worldwide.

What You'll Do

• Design, build, evaluate, and ship LLM-based solutions that give users more adaptive control over their listening experience

• Work on prompted playlist experiences with a focus on music fulfillment and session generation

• Collaborate with cross-functional partners across user research, design, data science, product, and engineering

• Prototype new ML approaches and bring them into production at global scale

• Build and improve systems that connect artists and fans in personalized and meaningful ways

• Contribute to the development of scalable ML systems serving hundreds of millions of users

• Promote best practices in ML system design, testing, evaluation, and deployment across the organization

• Actively contribute to a strong community of machine learning practitioners at Spotify

Who You Are

• You are experienced in machine learning and enjoy solving complex real-world problems in collaborative environments

• You have a strong background in machine learning, natural language processing, and generative AI

• You are comfortable applying theory to build real-world, production-ready applications

• You have hands-on experience building and deploying end-to-end ML systems at scale

• You are familiar with LLM-based systems and techniques for improving them using human feedback such as reinforcement fine-tuning, DPO, or similar approaches

• You have experience designing modular ML architectures and writing technical specifications in partnership with product teams

• You are experienced with large-scale distributed data processing tools such as Apache Beam or Apache Spark

• You have worked with cloud platforms like GCP or AWS

Where You'll Be

• This role is based in New York

• 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.

Additional information

The United States base range for this position is $184,050 $262,928 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.

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.

Discovered directly from the employer’s public Lever Postings API. Full public description, role lists and additional information were normalized into safe candidate-facing content.