Staff Machine Learning Engineer, Home Surfaces

United States Digital Space LLC; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

United States Digital Space LLC is seeking a Staff Machine Learning Engineer to shape the future of personalized discovery and engagement. You will work at the intersection of recommendation systems, large language models, and production-scale ML infra to build experiences that delight users at scale.

The role emphasizes taking models from research to production, setting technical direction in ambiguous domains, and solving complex personalization challenges for millions of listeners worldwide.

Job description

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 the company'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.

Surfaces Moments is a ML team within the Personalization Mission focused on creating moment-based experiences across the company surfaces. The team owns and evolves the experiences that help listeners quickly connect with the content that matters most to them, including the Home Shortcuts experience and the underlying intelligence that powers it. By combining cutting-edge machine learning, recommendation systems, and product thinking, the team delivers highly relevant, personalized experiences to millions of listeners around the world.

As a Staff Machine Learning Engineer, you will help shape the future of personalized discovery and engagement at the company. You'll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences that delight users and drive meaningful impact. This role is ideal for someone who enjoys taking models from research to production, driving technical direction in ambiguous problem spaces, and solving complex personalization challenges at global scale.