Why This Job is Featured on The SaaS Jobs
This Senior Machine Learning Engineer role sits at the intersection of product personalization and AI platform-building, a pattern increasingly central to modern SaaS businesses that compete on relevance and retention. Within Spotify’s Personalization org, the Zeitgeist focus on cultural understanding signals a mature, data-rich environment where ML systems are expected to translate real-world trends into user-facing experiences, not just offline model improvements.
For a SaaS career, the notable value is end-to-end ownership across the lifecycle that typically matters in subscription products: data pipelines, model training, production serving, monitoring, and evaluation. Work on LLM-enabled agentic workflows and tooling such as LLM-as-judge frameworks also reflects a broader SaaS shift toward operationalizing generative AI with measurable impact, building skills that transfer to other product-led platforms adopting similar stacks.
This role fits engineers who prefer ambiguity paired with accountability, and who enjoy collaborating across engineering, data science, and product to ship. It will particularly suit someone who wants to balance foundational ML infrastructure with direct influence on customer experience, and who is motivated by rigorous evaluation and reliability rather than experimentation alone.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job 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.\n
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
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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.
