Machine Learning Engineering Manager - Surfaces Music

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

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