Senior Machine Learning Engineer, Zeitgeist, Personalization

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music, podcasts, and listeners better than anyone else and by leveraging the latest in Generative AI. Join us and you’ll give millions of listeners great music and talk experiences, personalized to each and every one of them. 

The AI Foundation team within Personalization provides the state-of-the-art foundational data and tech with which we are inventing and shipping new interactive, personalized listening experiences. This is a team of about a hundred AI/ML Engineers, Applied Research Scientists, Product Managers, and domain experts. 

You’ll join the Zeitgeist squad within the AI Foundation team. We focus on building the systems and models that help Spotify understand culture in real time—what’s trending, why it matters, and how it shapes listening. You’ll leverage large language models and agentic workflows, and work closely with engineers, data scientists, and product partners to turn signals into meaningful user experiences. This is an exciting mix of platform-level content understanding and experience-level user presentation.

Design, build, and ship agentic systems that ground personalized listening experiences in cultural context and world knowledge, used by hundreds of millions of Spotify users

Develop and maintain pipelines for extracting, structuring, and serving cultural signals at scale, leveraging LLMs and agentic workflows

Partner closely with teams across Personalization to integrate foundational cultural data and tech into new agentic listening experiences

Own components end-to-end — from data pipelines and model training to production serving and monitoring

Design and build evaluation tooling (including LLM-as-judge frameworks and dataset analysis), and run experiments to evaluate the impact of cultural context signals on user experience and engagement

Help define the technical direction of the squad, contributing to architecture decisions, and shaping what building "0-to-1" experiences looks like in practice

You have 5+ years of experience building and shipping machine learning models end-to-end

You have a strong foundation in Python (Java and Scala are a plus) and experienced with GCP tools (e.g. Dataflow, BigQuery)

You have hands-on experience with LLMs and agent orchestration frameworks (e.g. LangChain, LlamaIndex, Pydantic), building tool-calling agents, RAG, and vector databases 

You have built and shipped production-scale, data-driven AI/ML systems, ideally in content understanding, knowledge graphs, NLP, MIR, or related domains

You are excited but not overhyped by the potential of Generative AI

You're comfortable operating as a 0-to-1 builder — you thrive in ambiguous, exploratory spaces and can move from idea to experimentation to production with confidence

You care about building inclusive, user-centric products, and you think about AI and ML in the context of products and user impact, not just tech

You have worked effectively in collaborative, cross-functional environments

You care deeply about code quality, reliability, and scalability

This role is based in New York

We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home