Machine Learning Engineer, GenRecs, Personalization
Join to apply for the Machine Learning Engineer, GenRecs, Personalization role at Spotify
Machine Learning Engineer, GenRecs, Personalization
Join to apply for the Machine Learning Engineer, GenRecs, Personalization role at Spotify
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The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix.
What You'll Do
* Design, build, evaluate, and ship ML solutions in Spotify’s personalization products
* Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways
* Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users
* Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
* Be part of an active group of machine learning practitioners
Who You Are
* An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment
* Strong background in machine learning, modern sequential model architectures, generative models, with hands on experience applying theory to develop real-world applications
* Hands-on expertise with implementing end-to-end production ML systems at scale in Python
* Additional systems experience (data and backend for ML Systems) in Java or Scala is a plus
* Experience with Pytorch and/or TensorFlow is required. Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams
* Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS
* Experience with technologies such as Kubernetes, Ray is a plus
* Experience troubleshooting model training and deployment across hardware ecosystems that mix CPU and GPU is a plus
* Experience optimizing model training and inference for different GPU types is a plus
Where You'll Be
* We offer you the flexibility to work where you work best! For this role, you can be within the European region as long as we have a work location.
* This team operates within the GMT/CET time zone for collaboration.
* Excluding France due to on-call restrictions.
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.
Seniority level
* Seniority level
Mid-Senior level
Employment type
* Employment type
Full-time
Job function
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