The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Daily Mix 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. We ask that our team members be physically located in Central European time or Eastern Standard/Daylight time zones for the purposes of our collaboration hours.

What You'll Do

• Improve the quality of Spotify’s personalized listening recommendations in playlists for our huge number of listeners, across many countries
• Define and implement standard methodologies for building and evaluating machine learning models for playlists
• Define the requirements for measuring and monitoring online ML model performance
• Provide technical leadership to machine learning engineers
• Collaborate with a multi-functional, agile team, 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 relevant ways
• Drive optimization, testing, and tooling to improve quality
• Be an active contributor to the Spotify group of machine learning practitioners

Who You Are

• You have contributed code and models to large-scale, production recommender systems
• You understand the architecture and development workflow for large-scale batch and streaming machine-learning systems
• You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with XGBoost, TensorFlow is also a plus
• You are comfortable writing SQL queries, exploring data, and developing good hypotheses for product improvements
• You understand a variety of machine learning algorithms, including online bandit models, learning to rank systems, and recommendation systems
• You may have experience with data pipeline tools like Apache Beam, Scio, etc., ML tools like Kubeflow, and cloud platforms like GCP or AWS
• You care about shipping product, agile software processes, reliability, and focused but fast experimentation
• You love your customers even more than your code

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.

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 with a community of more than 381 million users.