Senior Machine Learning Engineer - Policy & Safety
Spotify · Hybrid
MeritLog read this listing from Spotify's Lever job board and last checked it on September 9, 2026.
Source: the employer's Lever job board. Open the original listing for current details.
Job details
- Work model
- Hybrid
- Salary
- Not listed by source
- Location
- New York, NY; United States of America (Home Mix)
- Occupation
- Computer Systems Engineers/Architects(O*NET 15-1299.08)
Hiring context
How this role compares at Spotify
Spotify has 77 live roles in MeritLog’s catalog across 11 job families, and 26 of them are in data & analytics. 0 of those listings publish a pay range, a disclosure rate of 0%.
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Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
Job description
We design Spotify’s consumer experience-end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints-from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. The Policy & Safety team sits within Content Platform in the Experience Mission, building the systems that keep Spotify safe, compliant, and trusted by millions of users and creators. This team owns Spotify’s content moderation infrastructure - from detection models to policy enforcement systems and compliance data pipelines. Working at the intersection of machine learning, platform engineering, and regulatory compliance, the team partners closely with Trust & Safety, Legal, and Public Affairs. They’re on the critical path for every new content type and social feature - including messaging, comments, and collaborative experiences - ensuring safety is built in from day one. With a strong focus on “safety by default,” the team is investing in large-scale rearchitecture and ML-driven systems to proactively protect users and empower safer interactions across the platform.