The Home Podcasts team within Spotify’s Personalization Mission focuses on recommending podcasts on Spotify’s Homepage tailored to each user.
The team is looking for a Machine Learning Engineer who is passionate about personalization ML models and recommender systems, including contextual bandits, causal inference, deep learning, and generative recommenders.
The role involves being a technical leader within the team and Spotify, coordinating technical projects across teams, and facilitating collaboration with engineers, product owners, and designers.
The engineer will architect, design, develop, and deploy ML models for podcast recommendations across various surfaces.
The position requires being a leader in Home’s ML community and working collaboratively within existing platforms and systems.
Team members are expected to operate in the Eastern time zone for collaboration.
Requirements:
Candidates should 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 necessary.
Proficiency in feature engineering and building scalable data pipelines in Scio is essential.
A deep understanding of ML systems and infrastructure is required.
Experience with Tensorflow or PyTorch is necessary, and familiarity with Kubeflow and Ray is a plus.
Benefits:
The United States base salary range for this position is $176,166 to $251,666, plus equity.
Benefits include health insurance, six months of paid parental leave, a 401(k) retirement plan, and a monthly meal allowance.
Employees receive 23 paid days off and 13 paid flexible holidays, along with paid sick leave.
Spotify is committed to inclusivity and provides reasonable accommodations during the recruitment process.
