Machine Learning Engineering Manager, Personalization

Spotify; United States; On-site; USD 184,050 - 262,928; Full time

Benefits offered by this job

Health insurance

Six-month parental leave

401(k)

Meal allowance

Paid time off

Paid holidays

Paid sick leave

Job summary

Spotify is seeking a machine learning engineer to design and operate safety-focused ML systems across personalized surfaces such as recommendations and search. You will contribute to scalable safety platformization and work with both traditional ML models and generative AI, including integrating external and in-house models.

The role emphasizes collaboration with Product, Trust & Safety, and Content Platform, plus a rigorous, metrics-driven approach to improve performance, safety outcomes, and

Qualifications

• Experience building and deploying ML systems in production environments.
• Hands-on experience with traditional ML approaches and generative AI techniques.
• Experience with scalable backend systems requiring reliability and low latency.
• Ability to apply ML solutions to real-world product challenges, especially consumer-facing.
• Experience with model evaluation methods such as labeling workflows and ground-truth data generation.

Responsibilities

• Design, build, and improve ML systems powering safety in personalization surfaces.
• Contribute to platformization of safety systems for scalability and reuse.
• Develop and operate high-throughput, low-latency backend services.
• Collaborate with Product, Trust & Safety, and Content Platform to translate safety needs into technical solutions.
• Work on traditional ML models and generative AI, including integration of third-party and in-house models.
• Contribute to evaluation frameworks and ground-truth creation.
• Work with foundational model teams to embed safety into LLM-based experiences.
• Use metrics and experiments to improve system performance and user experience.

Skills

Production ML

Generative AI

Low latency

Consumer products

Model evaluation

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.

Equal Opportunity Employment

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.