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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.
About the Team
The Policy & Safety team sits within the Content Platform domain and builds the systems that keep Spotify safe and trustworthy at scale. We own the infrastructure behind content moderation, including detection models, policy enforcement systems, compliance pipelines, and the safety-by-default platform.
Our work sits on the critical path of every new content type and product experience—from messaging and comments to collaborative and agentic features. We partner closely with Trust & Safety, Legal, and Public Affairs to ensure that as Spotify evolves, safety is built in from the start—not added later.
What You Will Do
- Build and scale machine learning systems for proactive content detection, classification, and pre-publish safety scanning
- Design and implement policy evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops
- Develop multimodal models that combine text, audio, image, and video signals for safety and policy enforcement
- Architect feedback loops that turn human reviewer input into structured training data for continuous model improvement
- Translate regulatory requirements (e.g., precision/recall obligations, compliance reporting) into scalable ML system designs
- Partner with cross-functional teams across Trust & Safety, Legal, Public Affairs, and Product to deliver safe user experiences
- Drive technical direction in ambiguous problem spaces and contribute to long-term platform architecture
- Mentor and support other machine learning engineers, helping raise the bar across the team
Who You Are
- You have experience building and shipping production-grade machine learning systems at scale
- You have strong expertise in ML evaluation, including dataset design, metrics, and model performance monitoring
- You have worked with multimodal machine learning systems across text, audio, image, or video domains
- You are experienced with human-in-the-loop systems, active learning, or feedback-driven model improvement
- You are comfortable translating complex requirements into technical solutions, including regulatory or policy constraints
- You have experience working across teams and influencing technical direction in large-scale systems
- You are comfortable navigating ambiguity and making thoughtful decisions that balance speed, quality, and risk
- You communicate clearly and collaborate effectively with both technical and non-technical stakeholders
Where You Will Be
- This role is based in New York, NY
- 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.
What you need to know about the NYC Tech Scene
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
