Machine Learning Engineer - Personalization

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. We ask that our team members be physically located in the Central European time zone for the purposes of our collaboration hours.

We are looking for a Machine Learning Engineer (MLE II) to join our product area of hardworking engineers that are passionate about connecting new and emerging creators with users via recommendation algorithms. As an integral part of the squad, you will collaborate with engineers, research scientists, and data engineers in prototyping and productizing state-of-the-art ML models that allow us to find the right audience for content that is strategically important, such as fresh or timely content.

What You'll Do

• Develop and implement production systems that enrich and improve our listeners' experience on the platform
• Contribute to designing, building, evaluating, shipping, and refining Spotify's product by hands-on ML development
• Help drive optimization, testing, and tooling to improve quality of our recommendations
• Perform data analysis to establish baselines and inform product decisions
• Collaborate with a cross functional agile team spanning tech research, data science, product management, and engineering to build new technologies and features
• Stay up-to-date on the latest machine learning algorithms and techniques

Who You Are

• You have professional experience in applied machine learning
• Extensive experience working in a product and data-driven environment (Python, Scala, Java, SQL, or C++, with Python experience required) and cloud platforms (GCP or AWS)
• You have some hands-on experience implementing or prototyping machine learning systems at scale
• You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark
• You care about agile software processes, data-driven development, reliability, and disciplined experimentation
• You have experience and passion for fostering collaborative teams
• Experience with TensorFlow, pyTorch, and/or other scalable Machine learning frameworks
• Experience with building data pipelines and getting the data you need to build and evaluate your models, using tools like Apache Beam / Spark

Where You'll Be

• We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location (excluding France due to on-call restrictions) .
• This team operates within the Central European and GMT time zone for collaboration.

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.