Senior Machine Learning Engineer, Home Podcast

The Home Podcasts team within Spotify's Personalization Mission focuses on what podcasts to recommend on Spotify's Homepage and where, by building the model to rank and find the perfect podcast content, fully tailored to each user. We are looking for a Machine Learning Engineer who is passionate about personalization ML models, recommender systems and disciplines included but not limited to contextual bandits, causal inference, deep learning, and generative recommenders, which are actively used and expanded by our teams. Join us and you'll keep millions of users listening by making great recommendations to the Spotify Homepage. For the purposes of collaboration, we ask that our team members operate in the Eastern time zone.

What You'll Do

• Be a technical leader within the team you work with and within Spotify in general
• Coordinate technical projects across teams within SpotifyFacilitate collaboration with other engineers, product owners, and designers to solve interesting and challenging problems for delivering various media worldwide
• Be a valued member of an autonomous, cross-functional agile team
• Architect, design, develop, and deploy ML models that will serve podcast recommendations across the Home, Podcast Subfeed, and NPV surfaces
• Be a leader in Home's ML community and work collaboratively and efficiently within Home's existing platforms and systems.

Who You Are

• You have experience being a technical leader or mentorYou have a strong background in machine learning, especially experience with recommender systems
• You have experience in designing and building ML systems at Spotify (including experience in spotify-kubeflow and salem)
• You are experienced with feature engineering and building scalable data pipelines in Scio
• You have a deep understanding of ML systems and infrastructure
• You have experience in Tensorflow or PyTorch. Experience with Kubeflow, Ray is a plus.

Where You'll Be

• We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.
• This team operates within the Eastern Standard time zone for collaboration.