Why This Job is Featured on The SaaS Jobs
This Senior Machine Learning Engineer role sits in a part of the SaaS stack that is increasingly central for scaled consumer platforms: policy enforcement and content safety. Within a product that spans many surfaces and partner integrations, safety systems become shared infrastructure that must operate reliably across modalities, geographies, and evolving feature sets. The remit touches detection, classification, and enforcement, which are foundational capabilities for any subscription platform that hosts user and creator content.
From a SaaS career standpoint, the work builds durable expertise in production ML, not just model development. Owning evaluation frameworks, metrics, and continuous improvement loops maps closely to how mature SaaS organizations run ML as a long-lived product, with accountability for reliability, fairness, and operational outcomes. Cross-functional alignment with Trust and Safety, Legal, and Public Affairs also reflects a common SaaS reality: technical decisions frequently carry compliance and policy implications.
The role best suits an engineer who prefers end-to-end ownership, from data and modeling choices through to system design and stakeholder trade-offs. It will appeal to someone who enjoys translating policy requirements into measurable ML objectives and is comfortable influencing direction through technical leadership rather than narrow execution.
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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