Spotify

Lead the development of large-scale machine learning and recommendation systems powering Spotify’s Home feed, Shortcuts, and emerging AI experiences. This role combines recommendation modeling, LLM optimization, experimentation, and technical leadership at global scale.

Build and maintain Spotify’s native C++ desktop application across macOS and Windows, spanning platform capabilities, operating system integrations, delivery infrastructure, and backend services. The role also explores AI agent integrations within the Spotify Desktop experience.

Join Spotify’s Artist-First AI Music lab as a Senior Machine Learning Engineer building generative music experiences and LLM-driven features for artists and fans. You’ll develop production-scale ML pipelines, evaluation systems, and innovative listening experiences.

Build the secure enterprise AI infrastructure that connects models, agents, tools, and Spotify’s internal systems. You’ll own backend platform workstreams, agentic workflows, and AI governance capabilities at Spotify scale.

Lead the machine learning engineering strategy for Spotify’s Discovery Mode and royalty programs, building scalable systems that help artists reach more fans. This hands-on staff role combines technical leadership, production ML development, experimentation, and cross-functional collaboration.

Lead technical strategy and development for large-scale platform and partner experiences across Spotify, unifying systems and enabling multiple studios to move faster. You will also shape AI-native software development workflows while influencing architecture across organizational boundaries.

Build and evolve Groove, Spotify’s full-stack planning and collaboration platform, delivering high-impact features across frontend, backend, and infrastructure. You’ll own complex problems end-to-end, shape architecture, improve production reliability, and mentor engineers.

Own the machine learning systems behind Spotify’s personalized Home feed and emerging AI-powered experiences at global scale. You’ll combine recommendation systems, LLM training, experimentation, and technical leadership while mentoring other ML engineers.