Machine Learning Engineer

The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix.

The Context team’s mission is to own and innovate on contextual storytelling to enhance the listening experience. Using a mixture of human written content and LLMs, we strive to provide depth and connection for all listeners. We are looking for a Machine Learning Engineer to join our team to build and improve our storytelling capabilities.

What You’ll Do

Design, build, evaluate, and ship LLM based solutions that tell stories about our content and our users

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

Be part of an active group of machine learning practitioners

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 techniques 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 North America region as long as we have a work location.

This team operates within the Eastern Standard time zone for collaboration.

The United States base range for this position is $138,250- $197,500 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.

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.

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