Machine Learning Engineering Manager - Surfaces Music

Spotify•New York, NY

6d•Hybrid

About The Position

The Surfaces Music team builds the systems that power music recommendations across some of Spotify’s most visible experiences, including Home and the Now Playing view. We work across candidate generation, ranking, and embedding models to help listeners discover both new releases and deep catalog favorites. We’re also shaping the next generation of personalization through transformer-based models that bring more dynamic, context-aware recommendations to millions of listeners. You’ll collaborate closely with teams across Personalization, Experience, and Music to evolve how discovery works across Spotify.

Requirements

You have 5+ years of experience in software engineering or machine learning, including 2+ years supporting or leading a team
You have experience working on recommendation systems, including ranking, retrieval, or embedding-based approaches
You understand how to build and operate machine learning systems in production at scale
You are familiar with modern machine learning approaches such as deep learning or large language models
You have worked with cross-functional partners to deliver complex projects with multiple dependencies
You care about building products that are measurable, impactful, and grounded in user needs
You are comfortable working with experimentation and using data to guide decisions
You create an environment where collaboration, trust, and inclusion are prioritized
You stay engaged with technical decisions and enjoy supporting engineers in solving complex problems
You are curious about how AI tools can improve engineering workflows and team effectiveness

Responsibilities

Lead and support a team of Backend, Data, and Machine Learning Engineers building recommendation systems used by hundreds of millions of listeners
Set the technical direction for recommendation models across surfaces like Home and Now Playing
Guide the development of candidate generation, ranking, and embedding systems that improve music discovery
Partner with ML platform and infrastructure teams to evolve and scale generative recommendation models
Work closely with Product, Data Science, and Design to define success metrics and turn insights into meaningful product improvements
Ensure systems are reliable, efficient, and able to operate at global scale with low latency
Support strong engineering practices across experimentation, model evaluation, and production monitoring
Stay close to the technical work by reviewing architecture decisions and contributing to key discussions
Encourage thoughtful adoption of AI-assisted development tools to improve team productivity and reduce repetitive work
Create an inclusive, supportive team environment where engineers can grow and do their best work
Collaborate with peers across the organization to align on shared goals and technical direction