Staff Machine Learning Engineer (Safety & Policy)

Requirements

• You have experience building and shipping production-grade machine learning systems at scale

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• You are experienced with ML evaluation, including dataset design, metrics, and model performance monitoring

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• You have worked with multimodal machine learning across text, audio, image, or video domains

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• You have experience with human-in-the-loop systems, active learning, or feedback-driven model improvement

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• You are comfortable translating complex requirements into technical solutions, including policy or regulatory constraints

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• You are experienced working across teams and influencing technical direction in large systems

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• You are comfortable navigating ambiguity and making thoughtful trade-offs between speed, quality, and risk

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• You communicate clearly and collaborate effectively with both technical and non-technical partners

What the job involves

• 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

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• Our work is critical to every new content type and product experience—from messaging and comments to collaborative and emerging AI-driven features. We partner closely with Trust & Safety, Legal, and Public Affairs to ensure that safety is built into Spotify experiences from the start

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• Build and scale machine learning systems for proactive content detection, classification, and pre-publish safety scanning

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• Design and implement policy evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops

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• Develop multimodal models that combine text, audio, image, and video signals for safety and policy enforcement

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• Architect feedback loops that turn reviewer input into structured training data for continuous model improvement

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• Translate regulatory requirements into scalable ML system designs, including accuracy and reporting expectations

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• Partner with cross-functional teams across Trust & Safety, Legal, Public Affairs, and Product to deliver safe user experiences

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• Drive technical direction in ambiguous problem spaces and contribute to long-term platform architecture

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• Mentor and support other machine learning engineers, helping grow technical capability across the team

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