Design, build, and evaluate ML solutions for Spotify's personalization products, collaborating across teams to enhance user experience and model best practices.
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, natural language processing, and generative AI, with experience in applying theory to develop real-world applications
- Hands-on expertise with implementing end-to-end production ML systems at scale in Python, Java or Scala
- Experience with Pytorch and/or TensorFlow is a strong plusExperience 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
Where You'll Be
- We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.This team operates within the Eastern Standard time zone for collaboration.
The United States base range for this position is $138,250- $197,500 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.
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.
Top Skills
Apache Beam
Spark
AWS
GCP
Java
Python
PyTorch
Scala
TensorFlow
Similar Jobs
Artificial Intelligence • Cloud • Computer Vision • Hardware • Internet of Things • Software
As a Senior Machine Learning Engineer, you will develop scalable ML solutions, collaborate with teams, optimize models, and implement best practices for deployments and infrastructures.
Top Skills:
C++GoJavaKubernetesPythonPyTorchRayScalaSparkTensorFlow
AdTech • Big Data • Digital Media • Marketing Tech
As a Staff Machine Learning Engineer, you'll oversee data science systems, manage a team, and innovate AI/ML-based services with large-scale infrastructure for predictions in advertising.
Top Skills:
Caffe2GoKafkaKubernetesPrometheusPythonPyTorchScikit-LearnSpark MlTensorFlow
Information Technology • Software
As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.
Top Skills:
PythonPyTorchTensorFlow
What you need to know about the Boston Tech Scene
Boston is a powerhouse for technology innovation thanks to world-class research universities like MIT and Harvard and a robust pipeline of venture capital investment. Host to the first telephone call and one of the first general-purpose computers ever put into use, Boston is now a hub for biotechnology, robotics and artificial intelligence — though it’s also home to several B2B software giants. So it’s no surprise that the city consistently ranks among the greatest startup ecosystems in the world.
Key Facts About Boston Tech
- Number of Tech Workers: 269,000; 9.4% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Thermo Fisher Scientific, Toast, Klaviyo, HubSpot, DraftKings
- Key Industries: Artificial intelligence, biotechnology, robotics, software, aerospace
- Funding Landscape: $15.7 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Summit Partners, Volition Capital, Bain Capital Ventures, MassVentures, Highland Capital Partners
- Research Centers and Universities: MIT, Harvard University, Boston College, Tufts University, Boston University, Northeastern University, Smithsonian Astrophysical Observatory, National Bureau of Economic Research, Broad Institute, Lowell Center for Space Science & Technology, National Emerging Infectious Diseases Laboratories
