Machine Learning Engineer - Policy & Safety

We design Spotify's consumer experience,end to end, moment to moment, across every screen, platform, and partner integration.

The Policy & Safety team builds systems that keep Spotify safe and trustworthy at scale.

Responsibilities

• 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 reviewer input into structured training data for continuous model improvement
• Translate regulatory requirements into scalable ML system designs, including accuracy and reporting expectations
• 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 grow technical capability across the team

Requirements

• Experience building and shipping production-grade machine learning systems at scale
• Experience with ML evaluation, including dataset design, metrics, and model performance monitoring
• Experience with multimodal machine learning across text, audio, image, or video domains
• Experience with human-in-the-loop systems, active learning, or feedback-driven model improvement
• Comfortable translating complex requirements into technical solutions, including policy or regulatory constraints
• Experienced working across teams and influencing technical direction in large systems
• Comfortable navigating ambiguity and making thoughtful trade-offs between speed, quality, and risk
• Clear communication and collaboration with both technical and non-technical partners