Staff Machine Learning Engineer - Content Intelligence
UK Visa Sponsorship Analytics
Occupation TypeProgrammers and software development professionals
Occupation Code Skill LevelHigher Skilled
Sponsorship Salary Threshold
£54,700
(£28.05 per hour)
Occupation rate applies
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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.
What You Will Do
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.
What You Will Do
- Build and scale machine learning systems that generate deep understanding of content across modalities
- Develop models for classification, tagging, semantic understanding, and content enrichment
- Create high quality content enrichment at scale using LLMs and agentic systems.
- You have experience building and deploying machine learning systems in production
- You are comfortable working with ML frameworks such as PyTorch, TensorFlow, or similar
- You have experience working with large datasets and care about data quality and evaluation
- You are interested in or have worked with multimodal machine learning
- You understand how to design systems that balance automation with quality and user experience
- You are comfortable working on complex problems with evolving requirements
- You think in systems and understand how models connect to product outcomes
- You communicate clearly and work well across technical and non-technical teams
- 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.
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