spotify - Senior Machine Learning Engineer, Zeitgeist, Personalization
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Requirements
• 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 • ## Where You'll Be • This role is based in London or Stockholm • 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. • At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Responsibilities
• 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
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