Senior Staff Machine Learning Engineer - Agentic Systems

Spotify’s Personalization team is building the next generation of intelligent listening experiences. Across surfaces like DJ, Search, AI Playlists, and Home, we’re evolving from standalone features into a unified, agent-powered platform.

Generative AI is reshaping how we build products and systems. As part of this shift, we’re creating a shared Agent Engine that powers agent-based experiences across Spotify. You’ll join a cross-functional group of engineers, researchers, and product partners working at the intersection of distributed systems, machine learning, and user experience to shape how millions of listeners interact with audio every day.

Lead the technical architecture of Spotify’s Agent Engine, the shared runtime that powers agent-based experiences across the platform

Guide the transition of existing agent-powered features into a unified system, balancing speed, reliability, and real-world product constraints

Design how internal systems can be exposed as agent capabilities, enabling seamless integration across recommendations, search, catalog, and more

Build and improve evaluation systems that help teams measure quality, reliability, and user impact with confidence

Partner with research and machine learning teams to define what belongs in system design versus model capabilities

Explore and prototype new approaches to agent-based systems, and bring successful ideas into production at scale

Support best practices in building production-ready AI systems, including experimentation, observability, and performance optimization

Contribute to technical standards that help teams move faster while maintaining security and reliability

Stay connected to advances in the AI and machine learning community, and apply relevant ideas to Spotify’s products

You have experience building and scaling production AI systems, including systems powered by large language models

You are comfortable working across system design, infrastructure, and machine learning concepts, and enjoy connecting these areas

You have experience with areas such as agent orchestration, LLM infrastructure, evaluation systems, or data pipelines for machine learning

You have worked on platform or consolidation efforts that bring multiple systems or teams together

You are able to make progress in ambiguous problem spaces and bring structure to open-ended challenges

You communicate clearly and work well with engineering, product, and research partners across different levels of the organization

You stay informed on developments in AI and are motivated to apply new ideas in practical ways

You take a pragmatic approach to building, using prototypes and iteration to move ideas forward

You take ownership of outcomes and proactively manage risks, trade-offs, and expectations