Machine Learning Engineering Manager II

Spotify•Boston, MA

About The Position

The Samba team, part of Spotify's Personalization Mission, develops multi-objective balancing and sequencing systems for personalized listening experiences such as Autoplay, Radio, and Mixes. The team combines machine learning, backend engineering, and data engineering to support Spotify's financial objectives while keeping listeners at the center. The Engineering Manager will lead and develop the team and shape the technical direction of systems that influence how millions of people experience Spotify. This includes guiding machine learning and recommendation systems, aligning engineering strategy with listener needs and business goals, and supporting effective collaboration, planning, delivery, and technical decision-making.

Requirements

Experience managing and developing engineering teams, with a track record of helping people grow and delivering impactful results.
A strong technical background in machine learning, software engineering, or related areas, and the ability to confidently guide technical conversations.
Understanding of the opportunities and challenges of developing and operating machine learning systems in production.
Experience working with cross-functional partners to define priorities, navigate trade-offs, and deliver against shared goals.
Comfort leading teams through ambiguity and helping engineers make informed technical decisions.
Commitment to building inclusive, collaborative environments where people feel supported and empowered.
Clear communication, empathetic leadership, and the ability to balance people, technology, and business needs.

Responsibilities

Lead and grow a team of machine learning, backend, and data engineers, supporting their development and helping them deliver their best work.
Shape the team's technical strategy and execution, aligning engineering efforts with listener needs and Spotify's broader business goals.
Guide development of machine learning and recommendation systems that balance multiple objectives across personalized listening experiences.
Collaborate with Product and engineering partners to define priorities, align on outcomes, and translate complex challenges into clear plans.
Support thoughtful technical decisions across experimentation, scalability, system reliability, and model performance.
Create a culture of ownership, collaboration, and continuous improvement, empowering engineers to experiment and learn.
Drive effective planning and delivery while balancing immediate priorities with long-term technical health.
Foster an inclusive, supportive team environment through coaching, feedback, and thoughtful people leadership.