Description:

The Personalization team at Spotify aims to enhance the listening experience by providing tailored recommendations for music, podcasts, and audiobooks.
The team is responsible for critical assets that power recommendation and distribution across Spotify, utilizing modern AI techniques and large language models (LLMs).
As a Machine Learning Engineer, you will be a technical leader within your team and across Spotify, coordinating technical projects and facilitating collaboration with engineers, product owners, and designers.
You will architect, design, develop, and deploy machine learning models for podcast recommendations across various surfaces, including Home and Podcast Subfeed.
You will be a key member of an autonomous, cross-functional agile team and contribute to the ML community within the Home team.

Requirements:

You must have experience as a technical leader or mentor.
A strong background in machine learning, particularly with recommender systems, is required.
Experience in designing and building ML systems at Spotify, including familiarity with spotify-kubeflow and salem, is essential.
You should have experience with feature engineering and building scalable data pipelines in Scio.
A deep understanding of ML systems and infrastructure is necessary.
Proficiency in TensorFlow or PyTorch is required, and experience with Kubeflow or Ray is a plus.

The position offers a competitive salary range of $138,250 to $197,500, plus equity.
Benefits include health insurance, a six-month paid parental leave, and a 401(k) retirement plan.
Employees receive a monthly meal allowance, 23 paid days off, 13 paid flexible holidays, and paid sick leave.
Spotify is committed to inclusivity and provides reasonable accommodations during the recruitment process to support all candidates.