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 understood, 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 continue to grow—driven by advances in AI, new creation tools, and emerging content formats—we’re investing in intelligent systems that can understand, evaluate, manage, and route content reliably at global scale.
We’re hiring Senior Staff Machine Learning Engineers to build and scale foundational ML systems that power content understanding, safety, policy enforcement, and decisioning across Spotify. Depending on your experience and interests, you may focus on areas ranging from multimodal content understanding and platform-level ML systems to safety, risk detection, policy enforcement, and compliance.
You’ll help shape the architecture and technical strategy behind how Spotify understands and makes decisions about content at global scale. This work is foundational to delivering safe, high-quality experiences for listeners and creators while enabling new ways for people to interact with content across Spotify.
- Define and drive machine learning strategy across content understanding, safety, policy enforcement, and platform-level decisioning
- Build and scale production ML systems for classification, moderation, ranking, risk detection, and content evaluation
- Develop automated decisioning systems that support content quality, integrity, safety, and policy compliance at scale
- Design and deploy ML systems across multiple content modalities, including text, audio, image, and video
- Build systems capable of evaluating and making decisions about content in real time and at Spotify scale
- Enable controlled and reliable access to content and metadata for downstream products and applications
- Collaborate closely with Product, Policy, Trust & Safety, and Engineering teams to translate content standards into scalable technical solutions
- Advance automation while maintaining high standards for quality, safety, fairness, explainability, and reliability
- Provide technical leadership across teams, mentoring engineers and influencing ML engineering, evaluation, and system design best practices
- You have significant experience designing and building production-grade machine learning systems at scale
- You have deep experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or similar
- You have experience with—or a strong interest in—multimodal machine learning across areas such as text, audio, image, or video
- You have built systems where machine learning outputs inform or automate real-world decisions
- You understand how to balance automation with quality, safety, reliability, and user experience
- You’re comfortable tackling complex and ambiguous technical problems with significant product and business impact
- You think in systems, connecting models, data, infrastructure, and decisioning to platform-level outcomes and user experiences
- You care deeply about data quality, rigorous evaluation, fairness, explainability, and system reliability
- You’re an experienced technical leader who communicates clearly, mentors others, and can influence across technical and non-technical teams