Why This Job is Featured on The SaaS Jobs
Policy and safety infrastructure has become a defining capability for SaaS platforms that host user generated content and social features at global scale. This role sits in that layer, where content moderation is not a standalone product but a shared platform service that every new surface and integration depends on. The emphasis on “safety by default” signals work that is embedded early in feature development rather than added later as an operational patch.
For a SaaS career, the value is in building durable systems that combine machine learning with platform engineering and measurable governance. Experience designing evaluation loops, datasets, and online and offline metrics translates across many SaaS domains where model quality, reliability, and auditability matter. The cross functional interface with Trust and Safety, Legal, and Public Affairs also reflects a common SaaS reality: ML systems increasingly ship under policy constraints and external scrutiny.
This position fits an engineer who prefers end to end ownership of production ML, from model development to enforcement workflows and monitoring. It will suit someone comfortable making explicit trade offs in high impact systems and communicating those decisions to non engineering stakeholders. It is also well aligned with a senior profile that enjoys mentoring and shaping technical direction within a platform oriented team.
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 Policy & Safety team sits within Content Platform in the Experience Mission, building the systems that keep Spotify safe, compliant, and trusted by millions of users and creators. This team owns Spotify’s content moderation infrastructure — from detection models to policy enforcement systems and compliance data pipelines.
Working at the intersection of machine learning, platform engineering, and regulatory compliance, the team partners closely with Trust & Safety, Legal, and Public Affairs. They’re on the critical path for every new content type and social feature — including messaging, comments, and collaborative experiences — ensuring safety is built in from day one. With a strong focus on “safety by default,” the team is investing in large-scale rearchitecture and ML-driven systems to proactively protect users and empower safer interactions across the platform.
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What You'll Do
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Design, build, and ship production-grade machine learning systems that power content safety and policy enforcement at Spotify scale
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Own and lead key technical initiatives across detection, classification, and policy evaluation systems
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Develop and maintain ML models for content moderation, including multimodal and LLM-based systems
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Build robust evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops
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Drive experimentation to improve model performance, reliability, and fairness in safety-critical systems
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Collaborate closely with cross-functional partners in Trust & Safety, Legal, and Public Affairs to align on policy and enforcement needs
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Provide technical leadership within the team, mentoring engineers and contributing to ML strategy and prioritization
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Represent technical decisions and trade-offs in stakeholder discussions and influence product direction
Who You Are
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You have solid experience building and deploying machine learning systems in production environments at scale
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You are experienced with training, evaluating, and maintaining ML models using modern frameworks such as PyTorch
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You have a deep understanding of machine learning evaluation, including dataset design, metrics, and continuous improvement systems
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You know how to design systems that balance performance, reliability, and real-world impact in high-stakes domains
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You care about building safe, responsible, and user-centric ML systems
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You are comfortable working across disciplines, partnering with legal, policy, and product stakeholders
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You have experience leading technical projects and influencing direction within a team or product area
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You have experience with distributed systems or backend technologies (e.g., Scala)
Where You'll 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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