Why This Job is Featured on The SaaS Jobs
Personalization is a core SaaS lever because it directly influences discovery, retention, and perceived product quality at scale. This role sits in Spotify’s Personalization organisation, working on language understanding and content enrichment that feeds multiple surfaces like Home and Search. The focus on LLMs for structured, reusable knowledge reflects a broader SaaS shift from isolated models toward shared AI capabilities that power many product teams.
For a long-term SaaS career, the work builds durable strengths in end to end ML engineering rather than one off experimentation. It touches model selection and improvement techniques such as fine tuning and RAG, data pipelines, evaluation, and the operational concerns of distributing ML outputs reliably to internal consumers. Experience collaborating with backend and data engineering counterparts also maps well to how modern SaaS companies ship ML features as part of platform ecosystems.
This position is best suited to ML engineers who enjoy product grounded problem solving and care about measurement, tooling, and iteration. It will fit someone comfortable navigating ambiguous language data and turning it into assets other teams can build on. Candidates drawn to large scale systems, cross functional collaboration, and practical LLM deployment will find the scope aligned with their interests.
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 on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts, and audiobooks better than anyone else so that we can make great recommendations to every individual person and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like “Home” and “Search” as well as original playlists such as “Discover Weekly” and “Daily Mix.”
Personalization’s Minesweeper squad produces Human Understandable Language Knowledge to enrich music and talk content understanding. We use AI and ML techniques, including Large Language Models, to understand music, podcasts and audiobooks, building reliable, scalable systems to distribute that knowledge to Spotify internal teams, users, and creators. We are looking for a Machine Learning Engineer to join our team and help build the future of music, podcast and audiobook listening experiences for millions of listeners at Spotify. This is a unique opportunity to help develop and shape Spotify content enrichment, and recommendations. You’ll grow your skills in ML engineering at scale, work with a cross-functional team of Data Engineers, Backend Engineers, and researchers, and join a motivated and supportive team.
\n
What You'll Do- Utilize in-house and 3rd party LLMs to solve language understanding problems
- Employ techniques such as fine-tuning and RAG to improve modelsContribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development
- Help drive optimization, testing, and tooling to improve quality of our content enrichment assets
- Collaborate with cross-functional teams of MLEs, data and backend engineers, and other stakeholders including tech research, data science, and product to develop new features and technologies
- Be a participant in our AI Foundation’s ML community and work collaboratively and efficiently within our existing platforms and systems Perform data analysis to establish baselines and inform product decisions
- Stay up-to-date on the latest machine learning algorithms and techniques
Who You Are- You have a strong background in machine learning, especially experience with Large Language Models
- You have professional experience in applied machine learning
- Extensive experience working in a product and data-driven environment (Python, Scala, Java, SQL, with Python experience required) and cloud platforms (GCP or AWS)
- You have some hands-on experience implementing or prototyping machine learning systems at scale
- You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark
- You care about agile software processes, data-driven development, reliability, and disciplined experimentation
- You have experience and passion for fostering collaborative teams
- Experience with PyTorch, TensorFlow, and/or other scalable Machine learning frameworks. Experience with Ray or TFX is a plus
- Bonus if you have experience with architecting near real time pipelines
Where You'll Be- We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.This team operates within the Eastern Standard time zone for collaboration.
\n
The United States base range for this position is $138,250- $197,500 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.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
