At a Glance
- Tasks: Design and ship ML models that enhance user recommendations on Spotify.
- Company: Join Spotify, the world’s leading audio streaming service with a passion for music.
- Benefits: Flexible remote work, competitive salary, and a culture of inclusivity.
- Other info: Collaborative environment with opportunities for mentorship and career growth.
- Why this job: Make a real impact on how millions discover their next favourite song or podcast.
- Qualifications: Experience in building scalable recommendation systems and a strong understanding of the ML stack.
The predicted salary is between 70000 - 90000 £ per year.
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 Personalization (PZN) team is at the heart of how Spotify connects listeners with the content they love. Every day, hundreds of millions of people rely on the experiences we build, from Home and Search to Made For You and Discover Weekly.
Surfaces-NPV is an EU-based squad within the Surfaces Recommendations product area. We own recommendation quality on the Now Playing View, one of Spotify’s most personal and high-impact surfaces, and drive the introduction of new content verticals. We’re a small, senior-heavy team that values craft, autonomy, and shipping. We use AI coding tools such as Claude Code, experiment constantly, and believe the best ML engineers understand the full stack from user need to production system.
What You’ll Do
- Design, train, and ship machine learning models that power recommendations on the Now Playing View for hundreds of millions of users.
- Own ranking systems end-to-end, from experimentation and training pipelines to online serving and monitoring.
- Build and iterate on generative and agentic ML approaches to improve session steering and cross-content discovery.
- Work in an AI-native development environment, using AI tools to accelerate development while applying strong engineering judgment.
- Run A/B experiments, define success metrics, and translate improvements into measurable user impact.
- Collaborate closely with engineers, data scientists, researchers, and product managers to bring ideas into production.
- Shape the ML roadmap by identifying high-impact opportunities and mentor teammates.
Who You Are
- You have hands-on experience building recommendation or personalization systems at scale.
- You’re comfortable working across the ML stack, including pipelines, backend systems, and infrastructure.
- You think in products and understand how model decisions impact user experience.
- You’re fluent with AI-assisted development and use it to accelerate experimentation thoughtfully.
- You’re curious about emerging approaches like generative models and agentic ML systems.
- You’ve taken models from prototype to production and care about reliability and monitoring.
- You’re comfortable with ambiguity and enjoy defining new approaches in evolving problem spaces.
Where You’ll Be
This role is based in London. 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.
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.
Senior ML Engineer - Personalization (Now Playing, Remote) in London employer: Spotify
Contact Detail:
Spotify Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer - Personalization (Now Playing, Remote) in London
✨Tip Number 1
Network like a pro! Reach out to current or former Spotify employees on LinkedIn. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or contributions to open-source, make sure to highlight them. This is your chance to demonstrate your hands-on experience with recommendation systems and ML stacks.
✨Tip Number 3
Prepare for the interview by brushing up on your technical skills. Expect to discuss your experience with AI tools and how you've tackled real-world problems. Practice coding challenges and system design questions relevant to ML.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Spotify team and contributing to our mission.
We think you need these skills to ace Senior ML Engineer - Personalization (Now Playing, Remote) in London
Some tips for your application 🫡
Show Your Passion for Personalisation: When you're writing your application, let us see your enthusiasm for personalisation and recommendation systems. Share specific examples of projects you've worked on that relate to our mission of making listening easier and more enjoyable for users.
Be Clear and Concise: We appreciate clarity! Make sure your application is easy to read and straight to the point. Highlight your relevant experience and skills without going off on tangents. Remember, we want to know how you can contribute to our team!
Tailor Your Application: Don’t just send a generic application. Tailor it to the Senior ML Engineer role by mentioning specific technologies or methodologies from the job description. Show us that you understand what we do and how you fit into the picture.
Apply Through Our Website: Make sure to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our culture and values while you’re at it.
How to prepare for a job interview at Spotify
✨Know Your ML Stuff
Make sure you brush up on your machine learning knowledge, especially around recommendation systems. Be ready to discuss your hands-on experience with building and deploying models, as well as any specific projects you've worked on that relate to personalisation.
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've tackled ambiguous problems in the past. Spotify values engineers who can define new approaches, so think of examples where you've innovated or improved existing systems, particularly in a production environment.
✨Understand the User Experience
Since this role is all about enhancing user experience, be prepared to discuss how your model decisions impact users. Think about how you can articulate the connection between technical choices and user satisfaction during the interview.
✨Collaborate and Communicate
This position involves working closely with various teams, so highlight your collaboration skills. Prepare examples of how you've successfully worked with engineers, data scientists, and product managers to bring ideas to life, and be ready to discuss how you handle feedback and iterate on projects.