Description:

The Personalization team at Spotify is responsible for enhancing user experience by making decisions about what to play next easier and more enjoyable for listeners.
The team has developed popular features such as Blend and Discover Weekly by deeply understanding music and podcasts.
The role of Machine Learning Engineer II involves collaborating with engineers, research scientists, and data scientists to connect new creators with users through recommendation algorithms.
Responsibilities include designing, building, evaluating, shipping, and refining Spotify’s personalization products through hands-on machine learning development.
The engineer will work with a cross-functional agile team to create new product features that connect artists and fans in personalized ways.
The role requires prototyping new approaches and scaling solutions for hundreds of millions of active users.
The engineer will promote best practices in machine learning systems development and collaborate with a community of machine learning practitioners across Europe and Spotify.
The position also involves contributing to a creator-first vision and strategy to maintain Spotify's innovative edge in the field.

Requirements:

A strong background in machine learning is essential, with experience in bandit algorithms, large language models (LLMs), general neural networks, or other methods relevant to recommendation systems.
Hands-on experience in implementing production machine learning systems at scale using Java, Scala, Python, or similar languages is required.
Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn is a strong plus.
Experience with large-scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or Scio, as well as cloud platforms like GCP or AWS, is necessary.
A commitment to agile software processes, data-driven development, reliability, and disciplined experimentation is important.
A passion for customer satisfaction that surpasses a love for coding is essential.

The position offers flexibility to work from anywhere within the European region, as long as it aligns with Spotify's work location policies.
The team operates within the GMT/CET time zone to facilitate collaboration.
Note that France is excluded from this flexibility due to on-call restrictions.