Why This Job is Featured on The SaaS Jobs
Personalization is a defining differentiator for consumer SaaS, and this role sits at the point where modern recommendation systems and generative AI meet real product usage. The Prompted Playlists area signals a mature, high-scale platform context, where machine learning is not a lab function but a core part of how the service is experienced. Work centered on LLM-driven control and adaptive curation reflects a broader shift across SaaS toward more conversational, intent-based interfaces layered on top of long-lived user data.
For a SaaS-focused ML engineer, the career value here comes from end-to-end ownership in a production environment: moving from prototyping to shipping, then iterating through evaluation and feedback loops. Experience with human-in-the-loop improvement and distributed data tooling translates well to other SaaS companies building personalization, search, ranking, or AI assistants. Cross-functional collaboration with product, design, and research also builds the product judgment that often separates platform ML work from feature impact.
This role fits practitioners who enjoy turning ambiguous user intent into measurable system behavior and who prefer rigorous engineering practices around testing and evaluation. It is well suited to someone who wants their ML decisions to be exercised at large scale and is comfortable partnering broadly rather than working in isolation.
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 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.
Prompted Playlists (P2P) let listeners describe exactly what they want to hear and set the rules for their personalized playlist experience. This feature taps into a listener’s entire Spotify history—stretching back to day one—to reflect not just what they love now, but the full arc of their taste. The team blends personalization, world knowledge, and adaptive curation to deliver playlists that stay fresh, relevant, and delightful. P2P is looking for an experienced ML engineer to join the team!
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What You'll Do- Design, build, evaluate, and ship LLM based solutions that will enable our users to have more adaptive control of their content
- Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways
- Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users
- Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
- Be part of an active group of machine learning practitioners
Who You Are- An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment -
- Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications
- Hands-on expertise with implementing end-to-end production ML systems at scale. Experience with production LLM scale based systems is a plus
- Experience with incorporating human feedback to improve LLM based systems using technicals like DPO, KTO, and reinforcement fine-tuning
- Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams
- Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS
Where You'll Be- We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location.
- This team operates within the Eastern Standard time zone for collaboration.
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The United States base range for this position is $125,300 - $179,000 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. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. 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.
