Machine Learning Engineering Manager - Personalization
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Overview
Schedule
Full-time
Career level
Senior-level
Remote
Hybrid remote
Compensation
$184,050-$262,928/year
Benefits
Health Insurance
Paid Holidays
Paid Vacation
Job Description
Mission Statement
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.
About the Team
Safe-and-Sound is the centralized Safety team within the AI Foundations Studio in Personalization. We build machine learning systems that help ensure Spotify experiences and recommendations are safe, responsible, and enjoyable across core surfaces like Home, Search, as well as newer generative AI experiences.
We partner closely with Tech Research, Trust & Safety, and Content Platform to develop new approaches in areas like synthetic data, fairness, and responsible AI. Our focus is on building scalable, high-impact systems that support both today’s products and the next generation of AI-driven experiences.
What You'll Do
- Design, build, and improve machine learning systems that power safety across personalization surfaces such as recommendations, search, and emerging AI experiences
- Contribute to the platformization of safety systems, enabling scalable and reusable solutions across teams
- Develop and operate high-throughput, low-latency backend services powered by ML models
- Partner with Product, Trust & Safety, and Content Platform to translate safety needs into practical technical solutions
- Work on both traditional ML models and generative AI systems, including integrating third-party and in-house foundational models
- Contribute to evaluation frameworks, including labeling strategies, ground truth creation, and model validation approaches
- Collaborate with foundational model teams to embed safety into LLM-based and agent-driven experiences
- Use metrics and experimentation to continuously improve system performance, safety outcomes, and user experience
Who You Are
- You are experienced in building and deploying machine learning systems in production environments
- You have hands-on experience with both traditional ML approaches and newer generative AI techniques
- You have worked with scalable backend systems that require reliability, low latency, and high availability
- You understand how to apply ML solutions to real-world product challenges, ideally in consumer-facing products
- You have experience with model evaluation approaches such as labeling workflows, red-teaming, or ground truth data generation
- You are comfortable working across disciplines, collaborating with product managers, researchers, and policy partners
- You care deeply about building safe, responsible, and inclusive user experiences
- You bring a thoughtful, metrics-driven approach to problem solving and decision-making
Where You'll Be
- This role is based in New York or Boston
- We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home
The United States base range for this position is $184,050 – $262,928 USD, plus equity. The benefits available for this position include health insurance, six-month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, paid flexible holidays, and paid sick leave. These ranges may be modified in the future.
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.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
Automate your job search with Sonara.
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FAQs About Machine Learning Engineering Manager - Personalization Jobs at Spotify
What is the work location for this position at Spotify?
This job at Spotify is located in Boston, MA, according to the details provided by the employer. Some roles may also include multiple work locations depending on the requirement.
What pay range can candidates expect for this role at Spotify?
Candidates can expect a pay range of $184,050 and $262,928 per year.
What employment applies to this position at Spotify?
Spotify lists this role as a Full-time position.
What experience level is required for this role at Spotify?
Spotify is looking for a candidate with "Senior-level" experience level.
What benefits are offered by Spotify for this role?
Spotify offers following benefits: Health Insurance, Paid Holidays, Paid Vacation, Paid Sick Leave, Parental and Family Leave, and 401k Matching/Retirement Savings for this position. Actual benefits may vary depending on the employer's policies and employment terms.
What is the process to apply for this position at Spotify?
You can apply for this role at Spotify either through Sonara's automated application system, which helps you submit applications 10X faster with minimal effort, or by applying manually using the direct link on the job page.