Spotify
Staff Machine Learning Engineer - Content Intelligence
New York, NY · Staff+
Sponsorship not specified$227k-$325kDetected 77 days ago
Machine LearningData EngineeringLLMsA/B TestingDesign Systems
About the role
- 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.
- We ensure that everything from licensed catalog to user-generated content is trusted, safe, and high quality for millions of listeners worldwide.
- 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.
Responsibilities
- Develop models for classification, tagging, semantic understanding, and content enrichment
- Create high quality content enrichment at scale using LLMs and agentic systems.
- Design systems that make content intelligence signals available to downstream teams and products
- Collaborate with product, policy, and engineering teams to translate content intelligence into user impact
- Support rapid experimentation to prototype and launch new types of content signals
Skills
- Our systems are responsible for how content is ingested, understood, enriched, governed, and distributed across the platform.
- This work is central to delivering safe, high-quality, and differentiated experiences for millions of listeners and creators worldwide.
Compensation
- The United States base range for this position is 227,495-324,993 USD, plus equity.
- These ranges may be modified in the future.
Benefits
- Build and scale machine learning systems that generate deep understanding of content across modalities
- The benefits available for this position include health insurance, six-month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, and paid sick leave.
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This listing is sourced directly from Spotify's careers page and normalized into a canonical job model.