Spotify

Machine Learning Engineering Manager for the Personalization team, focusing on building and scaling ML systems for recommendations and safety across Spotify's core surfaces and generative AI experiences. The role involves managing teams that develop synthetic data, fairness, and responsible AI approaches.

Engineering Manager to lead a Research Engineering team focused on generative AI for music. The role involves managing engineers, owning the roadmap, ensuring efficient training pipelines, and transitioning research prototypes into production features for millions of users. The team works on state-of-the-art generative technologies for music, focusing on artist-first principles, partnerships, choice, fair compensation, and artist-fan connection.

Spotify is seeking a Backend Engineer for its Artist-First AI Music lab to design and build state-of-the-art generative music products. The role focuses on creating new listening experiences for fans and artists, emphasizing artist partnerships, fair compensation, and artist-fan connection through AI tools. The engineer will contribute to shipping AI-powered music products.

Senior Machine Learning Engineer focused on personalization for Spotify's music and podcast features, building recommendation systems and conversational playlist generation tools for hundreds of millions of users.

Senior Staff Machine Learning Engineer to build and scale foundational ML systems for content understanding, safety, and decisioning within Spotify's consumer experience. This role focuses on shaping the architecture and technical strategy for evaluating, governing, and safely delivering content globally, impacting both listeners and creators.

Director of Machine Learning Engineering for Spotify's Surfaces Foundation team, focusing on personalized recommendations for hundreds of millions of users. The role involves leading a team that shapes AI-driven recommendations across all Spotify surfaces, bridging ML infrastructure and product experience, and overseeing serving and evaluation systems.

Director of Machine Learning Engineering for Spotify's Surfaces Foundation team, focusing on personalized recommendation systems that power hundreds of millions of listeners. The role involves leading a group that shapes AI-driven recommendations across all Spotify surfaces, balancing ML infrastructure with product experience and enabling other teams to move faster.

Machine Learning Engineer for Spotify's Personalization team, focusing on improving music and podcast recommendations and search experiences. The role involves bringing emerging search and agentic experiences to a mature state, exploring, defining, building, validating, and optimizing new ideas.

Machine Learning Engineer role focused on personalization at Spotify, powering features like autoplay, radio, and personalized mixes. The role involves working on complex sequencing and optimization problems, balancing user preferences with business and creator needs, and delivering real-time personalization at scale. It blends machine learning, backend engineering, and data expertise.

Machine Learning Engineer on the Personalization team at Spotify, focusing on the Samba engine which powers recommendations, autoplay, radio, and personalized mixes. The role involves complex sequencing and optimization problems, balancing user preferences with business and creator needs, and delivering real-time personalization at scale.

Staff Machine Learning Engineer on the Personalization team at Spotify, focusing on building and productizing ML models for recommendation systems to improve user satisfaction and listening experience. The role involves collaboration with researchers and data scientists to enhance Home recommendation systems.

Staff Machine Learning Engineer on the Personalization team at Spotify, focusing on recommendation systems for music and podcasts to improve user satisfaction and engagement. The role involves prototyping and productizing state-of-the-art ML models.

Machine Learning Engineer for Spotify's Music Promotion team, focusing on building systems to understand and predict promotion performance for creators, providing actionable insights and data-driven solutions.

Machine Learning Engineering Manager for Spotify's Surfaces Music team, focusing on building and scaling systems for music recommendations across key experiences like Home and Now Playing. The role involves candidate generation, ranking, and embedding models, with a focus on transformer-based personalization for millions of listeners.

Machine Learning Engineer on the Personalization team at Spotify, focusing on recommendation systems to improve user satisfaction. The role involves designing, building, evaluating, integrating, and refining reward signals for recommendations, with a focus on A/B testing and integrating mid-term signals. Requires hands-on ML development and production ML systems experience.

Data Scientist role focused on generative music technologies within Spotify's Artist-First AI Music Lab. The role involves owning analytical projects, developing success metrics for generative features, analyzing A/B tests, performing exploratory analysis, building dashboards, and designing/running evaluations for generative music systems. Collaboration with Product, Design, Research, Marketing, and Engineering is key, with a focus on translating insights into product requirements and integrating evaluation signals into model development.

Security Engineer focused on protecting Spotify's platform and users, with a specific emphasis on securing AI/ML systems and generative AI applications. The role involves integrating security best practices, conducting threat modeling, evaluating security tools for AI/ML, and contributing to incident response for AI systems.

Senior Machine Learning Engineer role focused on optimizing ad loads for users on Spotify's audio and video platform. The role involves designing and implementing ML systems for ad performance, researching optimization strategies, analyzing user behavior with ML, and collaborating with cross-functional teams. Requires expertise in applied ML, software engineering, data analysis, Python/Java/Scala, Tensorflow/PyTorch, and data pipelines (Beam/Spark). Experience with LLMs, Ray, Adtech, or Recommender Systems is a plus.