Senior Machine Learning Engineer, Zeitgeist, Personalization

What You'll Do

• 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

Who You Are

• 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