Senior Machine Learning Engineer

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.

What You'll Do

• Build ML models used in generating candidates for music recommendations on Spotify's Home surface, including the Spotify Main feed and the music subfeed
• Build ML models used to order the music content appearing on carousel shelves on Spotify
• Collaborate with a cross functional agile team spanning user research, design, data science, product management, and engineering to build new user experiences that advance our mission to connect artists and fans in personalized and relevant ways
• Prototype new approaches and production-ize solutions at scale for our hundreds of millions of active users
• A/B test the ML models you've developed to understand how they improve the recommendations experience for our users
• Be part of an active group of machine learning practitioners collaborating with one another
• Occasional on-call monitoring and troubleshooting of production ML and backend systems

Who You Are

• You have a strong background in machine learning, with experience and expertise in personalized machine learning algorithms, especially recommender systems.
• You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with TensorFlow is also a plus.
• You have experience with data pipeline tools like Apache Beam or even our open source API for it, Scio and cloud platforms like GCP or AWS.
• You care about agile software processes, data-driven development, reliability, and disciplined experimentation
• You love your customers even more than your code

Where You'll Be

• We are a distributed workforce enabling our band members to find a work mode that is best for them!
• Where in the world? For this role, it can be within the Americas region in which we have a work location
• Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.
• Working hours? We operate within the Eastern Standard time zone for collaboration