Senior Staff Machine Learning Engineer

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

The Content Platform team powers the full lifecycle of content across music, podcasts, audiobooks, and emerging formats at Spotify.

We’re seeking a Senior Staff Machine Learning Engineer to build and scale ML systems that power safety, policy enforcement, and compliance across Spotify.

What You Will Do

• Define & drive machine learning strategy for safety, policy enforcement, and compliance systems
• Build and scale ML systems for detection, classification, and risk assessment across content
• Develop automated decisioning systems that ensure consistent, reliable enforcement of policies
• Design systems that support real-time and large-scale content evaluation
• Collaborate with product, policy, and trust & safety teams to operationalize content standards
• Improve automation to reduce manual intervention, maintaining high quality and safety standards
• Drive best practices in evaluation, fairness, and system reliability
• Mentor engineers and contribute to technical direction across teams

Who You Are

• You have strong experience building production-grade machine learning systems at scale
• You are experienced with modern ML frameworks such as PyTorch, TensorFlow, or similar
• You have worked on systems where ML outputs influence real-world decisions
• You understand how to design systems that balance automation with safety and user experience
• You are comfortable working on complex, ambiguous problems with high impact
• You think in systems and understand how models connect to platform-level outcomes
• You care about data quality, evaluation rigor, and system reliability
• You communicate clearly and influence across technical and non-technical teams

Where You Will Be

• This role is based in London or Stockholm
• 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.