Staff Machine Learning Engineer - Machine Learning Platform

Every day, hundreds of millions of people all over the world use Spotify to discover and listen to music and podcasts. We seek to understand the world of audio better than anyone else so that we can make excellent recommendations to every individual and keep the world listening. The product includes highly personalized surfaces as well as original playlists such as “Discover Weekly” and “Daily Mix” all powered by some of the most sophisticated machine learning algorithms.
As a member of our ML platform team, you will be working with a multi-functional team of engineers, product managers, user researchers, designers, and data scientists to craft the ML practitioner experience across Spotify. You will help build products that enable 60+ teams and 600+ Engineers to apply Machine Learning to initiatives that connect creators on Spotify’s platform (music, podcasts, and more) to the 400M+ and rapidly growing global user base.

What You'll Do

• Architect an outstanding platform to enable ML practitioners to analyze, monitor, debug, and communicate model performance, strengths and weaknesses. 
• Build libraries, tools, and playbooks for Offline Evaluation, A/B Testing, and Production Monitoring.
• Deliver scalable, testable, maintainable, and high-quality code.
• Share knowledge, evangelize best practices, and collaborate with teams working on pioneering ML problems.
• Join an encouraging environment fostering your individual growth through exciting work, offering the freedom to acquire new skills via hack weeks, reading groups, and a variety of internal training courses.

Who You Are

• You have an MSc or PhD in Statistics, Machine Learning, Computer Science, or equivalent with 5+ years of Applied ML experience.
• You understand the theory and practice of machine learning through hands-on experience delivering production machine learning systems at scale. 
• You have participated in the end-to-end model lifecycle (i.e, Problem Definition, Feature Engineering, Training, Hyperparameter Tuning, Offline Evaluation, Serving, and A/B Tests, and Monitoring).
• You have written fluent, modern Python and the ecosystem of libraries and frameworks used for Data and ML (e.g. Tensorflow, Kubeflow, Ray, Pytorch, Flyte, or XgBoost).
• You are knowledgeable about model evaluation approaches such as Counterfactual Analysis, Model Explainability, Model Calibration, Model A/B testing, and Multi-Arm Bandits. 
• You know the latest on research, industry products, and trends in the larger ML community. 
• You value team success over personal success.

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 Americas region in which we have a work location and is within working hours.
• Working hours? We operate within the Eastern Standard time zone for collaboration and ask that all be located in that time zone.
• Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.

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 with a community of more than 381 million users.


This role is not eligible for hire in Colorado, USA.