Senior Machine Learning Engineer, Personalization, Magenta

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. The Personalization team at Spotify makes deciding what to listen to next feel effortless for hundreds of millions of users — from Discover Weekly to our newest AI-powered experiences. We’re now building conversational AI capabilities that let users interact with Spotify in natural language. You’ll join a squad working at the core of this space, shaping how users discover and engage with audio through intelligent, responsive systems.

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.

What You'll Do

• Design and ship production-grade machine learning systems powering conversational and agentic AI experiences

• Build systems that interpret user intent, manage context across multi-turn interactions, and handle ambiguity reliably at scale

• Develop and evolve agentic workflows including memory, context management, and multi-step tool orchestration

• Create evaluation frameworks, including LLM-as-judge pipelines, to measure quality and guide iteration

• Partner closely with product, engineering, and design to deliver seamless, user-facing experiences

• Balance experimentation with production rigor, ensuring performance, latency, and reliability at Spotify scale

• Continuously improve agent behavior through tight feedback loops between evaluation and real-world usage

Who You Are

• You have 5+ years of experience building and shipping machine learning systems in production environments

• You are experienced with large language models and have worked on real-world applications beyond experimentation; shipped and maintained large scale systems with LLMs

• You have a deep understanding of challenges in conversational or agentic systems, such as context handling and multi-step reasoning

• You know how to evaluate ML systems rigorously and have experience designing metrics or evaluation pipelines

• You are comfortable debugging complex interactions between models, tools, and system constraints like latency

• You care about building reliable, scalable systems that deliver high-quality user experiences

• You enjoy working cross-functionally and contributing to a collaborative, inclusive team environment

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

Covesight rechecks every listing nightly. Last confirmed live: 2026-07-16.