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Machine Learning Engineering Intern

Machine Learning Engineering Intern
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Job Description

Spotify has more than 400M listeners in more than 180 markets around the world, who use our music, podcast, and audiobook services to find what delights, entertains, educates, and informs them. Personalization provides the technology to serve them what they expect to find, to help them explore and find new things to enjoy, and for us to suggest things they might not be aware of that they would like. As a result, from Blend to Discover Weekly, the Personalization team is behind some of Spotify’s most-loved features. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.

We are looking for a passionate Machine Learning Intern that will join the Content Promotion Platform team in Personalization. Our team is responsible for constantly improving our promotion recommendation system aiming at helping creators to grow their audience. You will work with our team to develop robust Machine Learning and Data solutions to contribute to that objective. You will investigate potential data sources and model architectures that could contribute to our main optimization objective and evaluate their usefulness. You will be able to improve upon both technical and business skills through industry experience that we hope to cater towards your career aspirations. Above all, your work will impact the way the world experiences music and podcasts.


What you'll do

  • Contribute directly to the Machine Learning and Data Pipelines that support our scoring model for content promotions

  • Assess and participate in the design decisions involving the future plans for the project

  • Develop your technical (Python, TensorFlow, Scala, SQL, Engineering fundamentals, etc) and soft skills

  • Participate in shadowing and mentoring opportunities with professionals

  • {optional} Work from any office we have across Europe

Who you are

  • You are interested in a career in Machine Learning and/or Data Engineering

  • You are a Masters or PhD level student in a relevant domain

  • You currently have valid work authorization to work in the country in which this role is based that will extend from June to August 2023

  • You have good knowledge of Machine Learning

  • You have a good level of programming, including experience with languages such as Python, TensorFlow, Scala, SQL

  • You have strong written and communication skills

  • You are excited and eager to learn

Where you'll be

  • We are a distributed workforce enabling our band members to find a work mode that is best for them!

  • Where in the world? For this role, it can be within the EMEA region in which we have a work location.

  • Prefer an office to work from instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.

  • Working hours? We operate within the Central European time zone for collaboration.


Our paid summer internships last for 10-13 weeks and start at the beginning of June. The last day to apply is February 13th, 2023 at 10 AM CET.

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.

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.

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