Spotify
Music
SeniorMachineLearningEngineer,Zeitgeist,Personalization
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer, Zeitgeist, Personalization at Spotify. Skills: Machine Learning, Generative AI, Large Language Models (LLMs), Agentic workflows, Python, GCP, Agent orchestration frameworks, RAG, Vector databases. Design, build, and ship agentic systems that ground personalized listening experiences in cultural context and world knowledge. Develop and maintain pipelines for extracting, structuring, and serving cultural signals at scale, leveraging LLMs and agentic workflows”
What You'll Achieve.
Give millions of listeners great music and talk experiences, personalized to each and every one of them; Turn signals into meaningful user experiences; Impact on user experience and engagement
Industry & Context.
Some in person meetings
What They're Looking For.
Must Have
5+ years of experience building and shipping machine learning models end-to-end, Foundation in Python, Experienced with GCP tools (e. g. Dataflow, BigQuery), Hands-on experience with LLMs and agent orchestration frameworks (e. g. LangChain, LlamaIndex, Pydantic), Building tool-calling agents, RAG, Vector databases, Built and shipped production-scale, data-driven AI/ML systems, Comfortable operating as a 0-to-1 builder, Care about building inclusive, user-centric products, Think about AI and ML in the context of products and user impact, Worked effectively in collaborative, cross-functional environments, Care deeply about code quality, reliability, and scalability
Nice to Have
Java and Scala are a plus, Ideally in content understanding, knowledge graphs, NLP, MIR, or related domains, Excited but not overhyped by the potential of Generative AI
What You'll Do.
and ship agentic systems that ground personalized listening experiences in cultural context and world knowledge
Develop and maintain pipelines for extracting
and serving cultural signals at scale
leveraging LLMs and agentic workflows
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)
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
How You'll Work.
Team & Collaboration
Partner closely with teams across Personalization; Work closely with engineers, data scientists, and product partners; Worked effectively in collaborative, cross-functional environments
Full Job Description
## Description 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. ## 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 impac
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