At a Glance
- Tasks: Design and build machine learning systems for personalised user experiences at scale.
- Company: Join Spotify's innovative Personalization team, shaping the future of music recommendations.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and engineering excellence.
- Why this job: Make a real impact on millions of users' listening experiences with cutting-edge technology.
- Qualifications: 5+ years in machine learning, data, or backend engineering with a focus on user experience.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- 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.
What the job involves
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.
- 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.
Machine Learning Engineer (Personalization, Samba) in London employer: Deepstreamtech
As a Machine Learning Engineer at Spotify, you will be part of a dynamic and innovative team that is dedicated to enhancing user experience through cutting-edge personalization features. With a strong emphasis on collaboration across disciplines and geographies, Spotify fosters a vibrant work culture that encourages creativity and continuous learning, offering ample opportunities for professional growth. Located in a thriving tech hub, employees benefit from a supportive environment that values diversity and inclusion, making it an exceptional place to advance your career while contributing to meaningful projects that impact millions of users worldwide.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer (Personalization, Samba) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Spotify or similar companies. Use LinkedIn to connect and engage with them; you never know who might have a lead on your dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to recommendation systems or optimization problems. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and problem-solving abilities. Practice coding challenges and be ready to discuss your past experiences with production-grade systems and scalable architectures.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Machine Learning Engineer (Personalization, Samba) in London
Some tips for your application 🫡
Show Off Your Experience:Make sure to highlight your 5+ years of experience in machine learning, data, or backend engineering. We want to see how you've tackled production-grade systems and scalable architectures, so don’t hold back on those details!
Talk About Your Projects:If you've worked on recommendation systems or optimisation problems, share specific examples! We love hearing about the projects that have shaped your T-shaped skillset across ML, data, and backend domains.
Emphasise Collaboration:Since we value teamwork, mention any cross-disciplinary collaborations you've been part of. Show us how you’ve navigated ambiguity and solved complex problems with others—this is key for our Personalization team!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Deepstreamtech
✨Know Your Stuff
Make sure you brush up on your machine learning concepts, especially around recommendation systems and optimization problems. Be ready to discuss your past experiences in detail, focusing on how you've tackled complex challenges and contributed to production-grade systems.
✨Showcase Your Collaboration Skills
Since the role involves working with cross-functional teams, prepare examples that highlight your ability to collaborate effectively. Think of times when you worked with product managers or data scientists to achieve a common goal, and be ready to share how you navigated any challenges.
✨Demonstrate User-Centric Thinking
The Personalization team cares deeply about user experience. Be prepared to discuss how your work has positively impacted users in the past. Share specific metrics or outcomes that demonstrate your understanding of balancing user satisfaction with business objectives.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Brush up on your knowledge of scalable architectures and backend engineering principles. You might be asked to solve a problem on the spot, so practice explaining your thought process clearly and concisely.
