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.
The Sessions Department within Personalization is building a portfolio of agentic and conversational products that define how hundreds of millions of people discover and experience audio, such as prompted Playlists or DJ, all powered by a single layer that understands music, culture, and the user’s taste
You'll join a team of four engineers actively building the agent and the strategy behind it. We work closely with the broader Sessions organization on one of the most highly-leveraged bets at Spotify right now: making it possible to have natural language conversation with Spotify across the entire app!
The team moves fast by staying hyper-focused: we pick a focused set of problems, ship new features to users weekly, and learn in the wild. We constantly dogfood our product and learn from users' data and feedback to find the most important next thing to build or improve, together.
What You'll Do
- You'll build and improve the core agentic capabilities that power the agent behind Talk to Spotify (memory, context management, multi-step tool use)
- You'll design and calibrate evaluation frameworks (including LLM-as-judge) that accelerate our confidence in the agent's behavior, and increase our offline-to-online success
- You'll work in a very dynamic space: the team prototypes, dogfoods, ships, learns, and refines in tight loops with real users, as our understanding of the problem and users' expectations of agentic products and Spotify evolve
Who You Are
- You're excited by agentic experiences — building agents, evaluating agents, and the hard problems in between (context handling, multi-step reasoning, ambiguity at scale)
- You like getting your hands dirty: shipping quickly, testing ideas against real usage, and learning from the wild rather than over-indexing on offline evaluation
- You have 5+ years of production ML experience deploying highly impactful products, or equivalent experience in other roles with a deep ML background
- You know how to evaluate ML systems rigorously — designing metrics, building eval pipelines, judge alignment, and can develop intuition through dogfooding and looking at user behavior
- You're comfortable debugging the messy interactions between models, tools, and system constraints like latency
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.
