Why This Job is Featured on The SaaS Jobs
This Staff Machine Learning Engineer role sits at a mature, high-scale consumer SaaS platform where “content intelligence” is a core capability rather than a supporting feature. The remit spans the content lifecycle across multiple formats, which makes the work materially different from single-domain recommendation or search projects. Building machine-readable understanding across audio, text, images, and video also reflects a broader SaaS trend toward multimodal ML systems that can operate reliably across heterogeneous inputs.
From a SaaS career perspective, the strongest signal here is systems thinking at production scale. The role touches the full chain from data pipelines and evaluation to serving signals that downstream product teams can depend on, which is central to how modern SaaS organisations operationalise ML. Experience with governance, quality, and safety oriented automation is also widely transferable across subscription products that need trust, consistency, and measurable impact.
This position is best suited to senior ML engineers who prefer platform-level problems over isolated model work and who enjoy partnering across engineering, product, and policy functions. It will appeal to professionals who like iterative experimentation but also care about reliability, clear evaluation, and making ML outputs usable across many internal consumers.
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 building intelligent systems that can evaluate, manage, and route content reliably at global scale.
We’re seeking a Staff Machine Learning Engineer to build and scale foundational ML systems that power content understanding across Spotify. In this role, you’ll work on systems that generate deep, machine-readable understanding of content across audio, video, text, and images—enabling automation, improving quality, and unlocking new product experiences. This work is central to delivering safe, high-quality, and differentiated experiences for millions of listeners and creators worldwide.
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What You Will Do
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Build and scale machine learning systems that generate deep understanding of content across modalities
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Develop models for classification, tagging, semantic understanding, and content enrichment
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Create high quality content enrichment at scale using LLMs and agentic systems.
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Design systems that make content intelligence signals available to downstream teams and products
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Improve automation for content quality, safety, and metadata enrichment at scale
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Collaborate with product, policy, and engineering teams to translate content intelligence into user impact
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Contribute to evaluation frameworks, data pipelines, and annotation systems
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Support rapid experimentation to prototype and launch new types of content signals
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Help improve system reliability, scalability, and performance across large datasets
Who You Are
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You have experience building and deploying machine learning systems in production
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You are comfortable working with ML frameworks such as PyTorch, TensorFlow, or similar
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You have experience working with large datasets and care about data quality and evaluation
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You are interested in or have worked with multimodal machine learning
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You understand how to design systems that balance automation with quality and user experience
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You are comfortable working on complex problems with evolving requirements
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You think in systems and understand how models connect to product outcomes
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You communicate clearly and work well 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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