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# Senior Machine Learning Engineer, Zeitgeist, Personalization

- **Company:** Spotify
- **Location:** Boston, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $184,050.0 - $262,928.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, BigQuery, Software Quality, Content Analysis, Data Flow Control, Graph Database, Python (Programming Language), Machine Learning, Natural Language Processing, Large Language Models, Multi-Agent Systems, Generative AI, Build Management, Data Analytics, Machine Learning Operations, Data Pipelines
- **Published:** May 16, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f665777ed787bc0c

## About the Role

Do you have experience in Python?, * 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

## Description

* 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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