Machine Learning Engineer, Personalization

Machine Learning Engineer, Personalization

Full-Time 70000 - 90000 € / year (est.) Home office (partial)
Dangote Industries Limited

At a Glance

  • Tasks: Design and build machine learning systems for personalised music recommendations.
  • Company: Join Spotify's innovative Personalization team, shaping the future of audio experiences.
  • Benefits: Flexible work options, inclusive culture, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on diversity and inclusivity.
  • Why this job: Make a real impact on millions of users' listening experiences with cutting-edge technology.
  • Qualifications: 5+ years in machine learning or backend engineering, with experience in recommendation systems.

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.

Samba sits at the heart of Spotify’s personalization engine, powering experiences like autoplay, radio, and personalized mixes. We work on complex sequencing and optimization problems—balancing what users love with how Spotify supports creators and the business.

Our team blends machine learning, backend engineering, and data expertise, and collaborates across North America and Europe to deliver impactful, real‑time personalization at scale.

What You'll Do

  • Design and build machine learning systems that optimize ranking and sequencing across personalized surfaces
  • Develop multi‑objective optimization strategies that balance user satisfaction with business outcomes
  • Collaborate closely with cross‑functional partners including product, data science, and engineering teams to align on goals, share context, and deliver impactful solutions
  • Work across ML, backend, and data layers to bring models into production
  • Contribute to scalable infrastructure supporting high‑volume user interactions
  • Run experiments and use insights to continuously improve performance
  • Help shape technical direction and raise the bar for engineering excellence within the team

Who You Are

  • You have 5+ years of experience in machine learning, data, or backend engineering
  • You are experienced with production‑grade systems and scalable architectures
  • You have worked on recommendation systems, ranking, or optimization problems
  • You bring a T‑shaped skillset across ML, data, and backend domains
  • You are comfortable navigating ambiguity and solving complex problems
  • You care about user experience and measurable impact
  • You enjoy collaborating across disciplines and geographies

Where You'll Be

This role is based in London or Stockholm. 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.

Machine Learning Engineer, Personalization employer: Dangote Industries Limited

Spotify is an exceptional employer that fosters a culture of inclusivity and collaboration, making it a fantastic place for Machine Learning Engineers to thrive. With the flexibility to work from either London or Stockholm, employees benefit from a dynamic work environment that encourages personal growth and innovation while contributing to impactful projects that enhance user experiences. Join us to be part of a team that values diverse perspectives and is dedicated to revolutionising the way the world listens.

Dangote Industries Limited

Contact Detail:

Dangote Industries Limited Recruiting Team

StudySmarter Expert Advice🤫

We think this is how you could land Machine Learning Engineer, Personalization

Tip Number 1

Network like a pro! Reach out to folks in the industry, especially those at Spotify. A friendly chat can open doors and give you insights that a job description just can't.

Tip Number 2

Show off your skills! Create a portfolio showcasing your machine learning projects, especially any related to recommendation systems. This is your chance to shine and demonstrate what you can bring to the table.

Tip Number 3

Prepare for the interview by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with production-grade systems and how you've tackled complex optimization problems.

Tip Number 4

Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in being part of the Spotify family.

We think you need these skills to ace Machine Learning Engineer, Personalization

Machine Learning
Backend Engineering
Data Expertise
Recommendation Systems
Ranking Optimization
Multi-Objective Optimization
Production-Grade Systems

Some tips for your application 🫡

Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Machine Learning Engineer role. Highlight your experience with recommendation systems and any relevant projects you've worked on, as this will show us you're a great fit for our team.

Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about personalisation in music and podcasts. Share specific examples of how you've tackled complex problems in the past, and how you can contribute to our mission at Spotify.

Showcase Your Collaboration Skills:Since we value teamwork, mention any cross-functional projects you've been part of. Let us know how you’ve worked with product, data science, or engineering teams to achieve common goals—this will help us see how you fit into our collaborative culture.

Apply Through Our Website:We encourage you to apply directly through our website. This ensures your application gets to the right people quickly, and it’s the best way for us to keep track of all the amazing talent out there. Don’t miss out on the chance to join our team!

How to prepare for a job interview at Dangote Industries Limited

Know Your Stuff

Make sure you brush up on your machine learning concepts, especially around recommendation systems and optimization strategies. Be ready to discuss your past projects and how they relate to the role at Spotify.

Show Your Collaborative Spirit

Spotify values teamwork, so be prepared to talk about how you've worked with cross-functional teams in the past. Share specific examples of how you’ve aligned goals and delivered impactful solutions together.

Emphasise User Experience

Since this role is all about enhancing user experience, think of ways you can demonstrate your understanding of user satisfaction. Bring examples of how your work has positively impacted users in previous roles.

Be Ready for Problem-Solving

Expect to tackle some complex problems during the interview. Practice articulating your thought process when faced with ambiguity and how you approach finding solutions, especially in machine learning contexts.