Machine Learning Engineer, II

Remote

Job Description

This job posting has expired and no longer accepting applications.

The Personalization team makes decisions about 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 are looking for a Machine Learning Engineer 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 scientists in prototyping and productizing state-of-the-art ML.

What You'll Do

Contribute to designing, building, evaluating, shipping, and refining Spotify’s personalization products by hands-on ML development

Collaborate with a cross 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

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 in Europe (and across Spotify) collaborating with one another

Together with a wide range of collaborators, help develop a creator-first vision and strategy that keeps Spotify at the forefront of innovation in the field.

Who You Are

You have a strong background in machine learning, enjoy applying theory to develop real-world applications, with experience and expertise in bandit algorithms, LLMs, general neural networks, and/or other methods relevant to recommendation systems

You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with TensorFlow, PyTorch, Scikit-learn, etc. is a strong plus

You have some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, 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