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.
What You'll Do
- 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
Who You Are
- 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
Where You Will Be
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.
Skills Required
- Strong experience building production-grade machine learning systems at scale
- Experience with modern ML frameworks such as PyTorch or TensorFlow
- Experience working on ML systems influencing real-world decisions
- Understanding of balancing automation with safety and user experience
- Ability to work on complex, ambiguous problems
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
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Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
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Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
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Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
Spotify Insights
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.
