Why This Job is Featured on The SaaS Jobs
At mature, consumer-scale SaaS platforms, trust and safety becomes a core product capability rather than a support function. This Senior Staff ML role sits in that layer, where policy, compliance, and user experience meet automated decisioning. The focus on the full content lifecycle across multiple formats signals work on platform primitives that must operate reliably across surfaces, partners, and geographies.
For a SaaS career, the distinctive value is learning how machine learning is operationalized when model outputs directly drive enforcement actions. That typically demands strong evaluation discipline, explainability, and careful handling of edge cases, plus an appreciation for how latency, throughput, and reliability shape real-time systems. Experience here tends to transfer to other SaaS domains where automated risk scoring, moderation, or governance is business critical.
This role fits senior engineers who prefer systems thinking over isolated modeling work, and who enjoy shaping technical direction across teams. It will appeal to professionals comfortable navigating ambiguity, aligning with non-ML stakeholders such as policy and product, and making pragmatic tradeoffs between automation coverage and safety outcomes at global scale.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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 Content Platform team powers the full lifecycle of content across music, podcasts, audiobooks, and emerging formats at Spotify. We ensure that everything from licensed catalog to user-generated content is trusted, safe, and high quality for millions of listeners worldwide. Our systems are responsible for how content is ingested, understood, enriched, governed, and distributed across the platform. As the scale and diversity of content continues to grow, driven by advances in AI and new creation tools—we’re investing in systems that ensure content remains safe, compliant, and high quality.
We’re seeking a Senior Staff Machine Learning Engineer to build and scale ML systems that power safety, policy enforcement, and compliance across Spotify. In this role, you’ll shape how automated systems evaluate and act on content—ensuring decisions are consistent, explainable, and reliable at global scale. This work is critical to maintaining trust for both listeners and creators.
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What You Will Do
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Define & drive machine learning strategy for safety, policy enforcement, and compliance systems
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Build and scale ML systems for detection, classification, and risk assessment across content
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Develop automated decisioning systems that ensure consistent, reliable enforcement of policies
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Design systems that support real-time and large-scale content evaluation
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Collaborate with product, policy, and trust & safety teams to operationalize content standards
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Improve automation to reduce manual intervention,maintaining high quality and safety standards
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Drive best practices in evaluation, fairness, and system reliability
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Mentor engineers and contribute to technical direction across teams
Who You Are
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You have strong experience building production-grade machine learning systems at scale
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You are experienced with modern ML frameworks such as PyTorch, TensorFlow, or similar
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You have worked on systems where ML outputs influence real-world decisions
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You understand how to design systems that balance automation with safety and user experience
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You are comfortable working on complex, ambiguous problems with high impact
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You think in systems and understand how models connect to platform-level outcomes
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You care about data quality, evaluation rigor, and system reliability
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You communicate clearly and influence across technical and non-technical teams
Where You Will Be
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This role is based in London or Stockholm
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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.
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