ML Software Engineering Manager

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., Lead, mentor, and grow a team of ML software engineers, fostering a culture of inclusion, ownership, and technical excellence

Define and drive the technical vision for Apple Music’s recommendation and personalization systems, enabling fast model iteration at scale

Architect and oversee the development of high-quality, production-grade ML systems and personalization experiences

Partner cross-functionally with Research, Engineering, and Product teams to align on requirements, priorities, and system design

Communicate clearly with stakeholders on project status, technical tradeoffs, API design, and system architecture

Recruit and develop top engineering talent, maintaining Apple’s standard of excellence and commitment to diversity

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

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

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $206,200 and $356,400, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

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.