Spotify
Personalization
SeniorMachineLearningEngineer,Zeitgeist,Personalization
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“Senior Machine Learning Engineer, Zeitgeist, Personalization at Spotify. Skills: Machine Learning, Agentic Systems, LLMs, Personalization, Data Pipelines, Production ML. 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”
What You'll Achieve.
keep millions of users listening by making great recommendations to each and every one of them; ground personalized listening experiences in cultural context and world knowledge, used by hundreds of millions of Spotify users; evaluate the impact of cultural context signals on user experience and engagement
Industry & Context.
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
used by hundreds of millions of Spotify users
Develop and maintain pipelines for extracting
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
How You'll Work.
Team & Collaboration
Partner closely with teams across Personalization to integrate foundational cultural data and tech into new agentic listening experiences; 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 and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. The Personalization team at Spotify makes deciding what to listen to next feel effortless for hundreds of millions of users — from Discover Weekly to our newest AI-powered experiences. We’re working on partners integrations making sure Siri, Alexa, Meta glasses, OpenAI (and many more! plus a few exciting launches next weeks) are yet another way for users to discover Spotify audio capabilities. Moreover - promptable playlist, chat integration - you’d join the squad owning the foundations of the search agent system powering those latest functionalities. ## 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
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