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. We ask that our team members be physically located in Central European time or Eastern Standard/Daylight time zones for the purposes of our collaboration hours.
We are looking for a Senior Machine Learning Engineer to join our team. We are at the forefront of deriving foundational knowledge through vectors and representations of music content and users’ taste, which are key for Spotify teams to create personalized, engaging experiences. This is a unique opportunity to help develop and improve our next-gen user representations, and shape the way Spotify recommendations work. You’ll build machine learning products at scale, drive a ton of business impact, and join a high-energy, positive team environment!
Join us and you’ll keep millions of users listening to great recommendations every day.
What You’ll Do
- Contribute to designing, building, evaluating, shipping, and refining Spotify’s personalization products by hands-on ML development
- Collaborate with a cross functional agile team 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 relevant 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 model development, testing, evaluation, etc., both inside the team as well as throughout the organization
- Be part of an active group of machine learning practitioners across Spotify
Who You Are
- You have a strong background in machine learning, enjoy applying theory to develop real-world applications, with experience and expertise in machine learning algorithms for recommender systems
- Familiarity or a hands-on experience with representation learning is a big plus
- You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages
- Experience with TensorFlow, PyTorch, XGBoost, etc is a strong plusYou have experience with data pipeline tools like Apache Beam, Scio, etc., and cloud platforms like GCP or AWS
- You care about agile software processes, data-driven development, reliability, and disciplined experimentation
- You routinely survey research publications in the machine learning and software engineering communities
- You love your customers even more than your code
Where You’ll Be
- We offer you the flexibility to work where you work best! For this role, you can be within the Eastern time zone as long as we have a work location.
The United States base range for this position is $171,903.00- 245,575.00 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.
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.
