Machine Learning Engineer, Amazon Music ML & Personalization

Amazon Music is revolutionizing the audio entertainment landscape as an immersive service that strengthens connections between fans, artists, and creators. As a Machine Learning Engineer on the PRISM team, you'll be at the forefront of developing personalized recommendations across Amazon Music's diverse content portfolio including music, audiobooks, podcasts, and live streaming.

The role offers an exciting opportunity to shape the experiences of millions of users worldwide through innovative machine learning solutions. You'll work alongside world-class engineers and scientists, leveraging Amazon's massive scale of data to build and deploy sophisticated recommendation models that delight customers globally.

Your responsibilities will span the entire development lifecycle - from ideation to production deployment. You'll collaborate closely with product managers and applied scientists to create meaningful, personalized recommendations for Amazon Music's global customer base. The team owns critical infrastructure including data pipelines for training data generation, feature engineering, and an approximate nearest neighbor service.

Key technical aspects include:

• Developing novel ML solutions for content recommendations
• Implementing and validating ideas through A/B testing
• Managing ML code and data pipelines
• Training and deploying models at scale
• Collaborating on experimentation and production workloads

The position offers competitive compensation ranging from $129,300 to $223,600 based on location, plus potential equity, sign-on bonuses and comprehensive benefits. You'll be part of Amazon Music's mission to innovate at the intersection of music and culture, working on features from personalized playlists to exclusive content and concert livestreams.

This is an excellent opportunity for a machine learning engineer who wants to make a significant impact on how millions of people discover and enjoy audio content while working with cutting-edge ML technologies and massive datasets in a fast-paced, collaborative environment.