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 investing in systems that ensure content remains safe, compliant, and high quality.
We’re seeking a Senior Staff Machine Learning Engineer to build and scale ML systems that power safety, policy enforcement, and compliance across Spotify. In this role, you’ll shape how automated systems evaluate and act on content—ensuring decisions are consistent, explainable, and reliable at global scale. This work is critical to maintaining trust for both listeners and creators.
What You Will Do
Define & drive machine learning strategy for safety, policy enforcement, and compliance systems
Build and scale ML systems for detection, classification, and risk assessment across content
Develop automated decisioning systems that ensure consistent, reliable enforcement of policies
Design systems that support real-time and large-scale content evaluation
Collaborate with product, policy, and trust & safety teams to operationalize content standards
Improve automation to reduce manual intervention,maintaining high quality and safety standards
Drive best practices in evaluation, fairness, and system reliability
Mentor engineers and contribute to technical direction across teams
Who You Are
You have strong experience building production-grade machine learning systems at scale
You are experienced with modern ML frameworks such as PyTorch, TensorFlow, or similar
You have worked on systems where ML outputs influence real-world decisions
You understand how to design systems that balance automation with safety and user experience
You are comfortable working on complex, ambiguous problems with high impact
You think in systems and understand how models connect to platform-level outcomes
You care about data quality, evaluation rigor, and system reliability
You communicate clearly and 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.
