Machine Learning Engineer II - Personalization
Spotify
- Location
- United States of America
- Posted
- Salary Range
- 138k - 198k USD
ML Solutions Engineer for Spotify's Personalization team
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Machine Learning Engineer II - Personalization
Spotify
Job Location
Salary Range
138k - 198k USD / YEAR
Job Summary
The Personalization team at Spotify designs and builds machine learning solutions for safety in personalization products. The role involves collaborating with cross-functional teams to build new product features, prototyping new approaches, and promoting best practices of ML systems development. As an experienced ML practitioner, you'll work on complex real-world problems in a fast-paced environment, applying theory to develop real-world applications. You'll have the flexibility to work from anywhere within the North America region, with a competitive salary range of $138,250-$197,500 plus equity. The benefits package includes health insurance, paid parental leave, and 401(k) retirement plan. With a strong background in machine learning, natural language processing, and generative AI, you'll be part of an active group of machine learning practitioners collaborating with one another.
The Personalization teams make deciding what to play next easier and more enjoyable for every listener. From Daily Mix 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.
Fritz works on recommending the content someone is most likely to listen to next from their listening history. The underlying machine learning model makes predictions for most requests to Home in real time and operates on sequences based on NRT interactions as its input! The team is migrating its training infrastructure to Ray and will continue innovating to try and increase consumption from Home and overall for Spotify.
What You'll Do
Design, build, evaluate, and ship ML solutions for safety 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 in New York (and across Spotify) collaborating with one another
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 Java, Scala, Python, or similar languages. Experience with Pytorch, TensorFlow, Scikit-learn etc is a strong plus
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
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.
