Machine Learning Engineer II - Personalization - Remote

Overview

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.

In Short

• 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

Requirements

• 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

Benefits

• 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.
• This team operates within the Central European time zone for collaboration.