The Role
The role involves designing and building machine learning systems for personalized user experiences by leveraging cultural understanding using LLMs. You'll manage data pipelines and collaborate across teams to integrate foundational AI tech.
Summary Generated by Built In
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 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
Where You'll Be
- This role is based in New York
- 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
The United States base range for this position is $184,050- $262,928 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
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.
Skills Required
- 5+ years of experience building and shipping machine learning models end-to-end
- Strong foundation in Python (Java and Scala are a plus)
- Experience with GCP tools (e.g. Dataflow, BigQuery)
- Hands-on experience with LLMs and agent orchestration frameworks (e.g. LangChain, LlamaIndex, Pydantic)
- Experience building production-scale AI/ML systems in content understanding, knowledge graphs, NLP, or related domains
- Experience working in collaborative, cross-functional environments
- Strong focus on code quality, reliability, and scalability
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
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Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
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Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
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Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
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The Company
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.
