SpotifyMachine Learning Engineering Manager II
Spotify is hiring a Machine Learning Engineering Manager II for a remote role open to applicants in United States. Level rates it ; you can apply on Spotify.
Level's read on this job
- Builds AI ●●●● is how Level rates this machine learning engineer role: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include machine learning, recommendation systems and engineering management.
- Level lists 84 other open roles at Spotify.
- The role was posted in the last few days.
AI in this role
Lead a machine learning engineering team developing multi-objective personalization and recommendation systems at Spotify.
The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.
At Spotify, great personalization is about more than recommending the right song. It's about creating listening experiences that feel effortless and meaningful while balancing the needs of listeners, creators, and our business.
The Samba team, part of Spotify's Personalization Mission, works on multi-objective balancing and sequencing for personalized listening experiences like Autoplay, Radio, and Mixes. Our engineers bring together machine learning, backend engineering, and data engineering to build systems that support Spotify's financial objectives while keeping listeners at the center of what we do.
We're looking for an Engineering Manager to lead this team, develop our engineers, and help shape the technical direction of systems that influence how millions of people experience Spotify.
What You'll Do
- 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, ensuring engineering efforts align with listener needs and Spotify's broader business goals.
- Guide the 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 the team in making thoughtful technical decisions across experimentation, scalability, system reliability, and model performance.
- Create a culture of ownership, collaboration, and continuous improvement, where engineers feel empowered 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.
Who You Are
- You have experience managing and developing engineering teams, with a track record of helping people grow and delivering impactful results.
- You bring a strong technical background in machine learning, software engineering, or related areas, and can confidently guide technical conversations.
- You understand the opportunities and challenges of developing and operating machine learning systems in production.
- You have experience working with cross-functional partners to define priorities, navigate trade-offs, and deliver against shared goals.
- You are comfortable leading teams through ambiguity and helping engineers make informed technical decisions.
- You care about building inclusive, collaborative environments where people feel supported and empowered.
- You communicate with clarity, lead with empathy, and know how to balance people, technology, and business needs
Where You'll Be
- We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location.
- This team operates within the Eastern Standard time zone for collaboration.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens. At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
How we rate this
Machine Learning Engineering Manager II at Spotify rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- Typical machine learning engineer job
- $198k
This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where recommendation systems was part of your work. What did you do?
- Tell me about a project where engineering management was part of your work. What did you do?
- Tell me about a project where backend engineering was part of your work. What did you do?
- Tell me about a project where data engineering was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Machine learning, Recommendation Systems, Engineering Management, Backend Engineering, and Data Engineering. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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