Skip to content
S
Spotify

Senior Machine Learning Engineer - Policy & Safety

Machine Learning Engineering
New York, NYhybridPermanent

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.

Explore related Data & AI jobs

Compare this role with machine learning engineer jobs and open roles at Spotify.

You can also browse all Data & AI jobs to find similar openings by category, seniority, remote setup, and location.

Other jobs at Spotify

Spotify has no open Data & AI positions right now.

Browse all jobs
© Dataaxy. All rights reserved.Job data is gathered from publicly available sources or contributed by users.