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ML Software Engineering Manager - Apple Music

Apple, Inc.Cupertino, CA🇺🇸United StatesPosted 12 Aug 2026

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. The work you do here has the potential to reach hundreds of millions of people, and nowhere is that more true than Apple Music, where technology and artistry meet to connect listeners with the music they love.

The Apple Music ML team is looking for an ML Software Engineering Manager to lead a team at the forefront of music recommendation and personalization. This is a rare opportunity to shape how tens of millions of people discover music every day, working alongside world-class engineers, researchers, and product partners to build systems that are both technically ambitious and deeply human.

Description

As an ML Software Engineering Manager on the Apple Music team, you will lead a team of talented engineers responsible for designing, building, and shipping state-of-the-art music recommendation and personalization systems. You will own the technical direction of the team, partner closely with Research and Product, and ensure your engineers have the support, clarity, and growth opportunities they need to do the best work of their careers.

You will collaborate regularly with Apple teams globally, working across time zones to deliver cohesive, high-quality experiences at scale.

Minimum Qualifications

3+ years of experience managing software engineering teams

5+ years of experience building and shipping high-quality software products

Hands-on experience designing and shipping products in the recommendation systems space

Strong computer science fundamentals and system design experience

Bachelor's or Master's degree in Computer Science or a related field, or equivalent practical experience

Preferred Qualifications

Deep understanding of modern recommendation system architectures and the tradeoffs involved in building them at scale

Experience with machine learning frameworks and familiarity with end-to-end ML model development lifecycles

Demonstrated ability to translate complex technical tradeoffs for both technical and non-technical audiences

Experience leading or collaborating with geographically distributed teams across time zones

Track record of attracting, developing, and retaining strong engineering talent in a competitive environment

Passion for music and a curiosity about how technology can deepen the connection between people and the artists they love

Skills

Machine Learning

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