As Spotify grows its video catalog, understanding and classifying visual content in our catalog becomes very important to support moderation, search and recommendation use cases. We are a small, cross-functional team of Machine Learning Engineers and Data Engineers leveraging state of the art machine learning solely focused on building and deploying visual understanding models.

Locations

London
Stockholm

Job type

Permanent

Delivering the best Spotify experience possible. To as many people as possible. In as many moments as possible. That’s what the Experience team is all about. We use our deep understanding of consumer expectations to enrich the lives of millions of our users all over the world, bringing the music and audio they love to the devices, apps and platforms they use every day. Know what our users want? Join us and help Spotify give it to them.

As a Machine Learning Engineer in our Content Understanding teams, you will help define and build ML deployed at scale in support of a broad range of use cases driving value in media and catalog understanding.

What You'll Do

Build production systems that enrich and improve our listeners’ experience on the platform
Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development
Prototype new approaches and production-ize solutions at scale for our hundreds of millions of active users
Help drive optimization, testing, and tooling to improve quality
Perform data analysis to establish baselines and inform product decisions
Collaborate with a cross functional agile team spanning design, data science, product management, and engineering to build new technologies and features

Who You Are

You have professional experience in applied machine learning
Extensive experience working in a product and data-driven environment (Python, Scala, Java, SQL, or C++, with Python experience required) and cloud platforms (GCP or AWS)
You have some hands-on experience implementing or prototyping machine learning systems at scale
You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark
You care about agile software processes, data-driven development, reliability, and disciplined experimentation
You have experience and passion for fostering collaborative teamsExperience with TensorFlow, pyTorch, and/or Google Cloud Platform is a plus
Experience with building data pipelines and getting the data you need to build and evaluate your models, using tools like Apache Beam / Spark is a plus

Where You'll Be

You will work out of our London office

Link copied to clipboard.

Our global benefits

Extensive learning opportunities, through our dedicated team, GreenHouse.

Flexible share incentives letting you choose how you share in our success.

Global parental leave, six months off - fully paid - for all new parents.

All The Feels, our employee assistance program and self-care hub.

Flexible public holidays, swap days off according to your values and beliefs.

Learn about life at Spotify

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 with a community of more than 500 million users.

Learn more about our culture