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Spotify's consumer experience is designed end-to-end, moment-to-moment, across every screen, platform, and partner integration, with the mission to make listening feel effortless, personal, and joyful for billions of users worldwide. The Content Platform team aims to power the best audio network in the world by enabling creators and content distributors to deliver their content frictionlessly and equip Spotify teams with the richest possible catalog. The Annotation Platform Ops team works alongside Product and Engineering teams at the heart of the Content Platform R&D studio to develop rich, interconnected datasets that enable delightful consumer experiences through a fusion of machine and human expertise, focusing on use cases across all of Spotify’s domains - Music, Podcasts, and Audiobooks. We are seeking an Annotation QA Analyst to help create a broad collection of labeled datasets that Content Platform teams use to train, evaluate, and better understand models and systems. This is an exciting opportunity to work at the center of ML and AI-driven development.
Responsibilities
Review annotated data to ensure it meets Spotify’s quality standards and policies, prioritizing work based on business needs
Deliver high-quality, timely results for Product and Engineering teams using established QA frameworks and metrics such as agreement rates and consensus
Handle complex edge cases, helping define ground truth and reduce ambiguity across datasets
Identify patterns, insights, and areas for improvement, and communicate findings clearly to both technical and non-technical partners
Contribute to feedback loops between annotation teams, R&D collaborators, and content policy experts to improve workflows and outputs
Help develop and refine annotation guidelines, supporting annotator training and continuous improvement
Collaborate closely with teammates across multiple projects and domains
Requirements
Experience working with annotation, data quality, or QA processes in ML/AI environments
Familiarity with LLM or AI-driven annotation workflows and human-in-the-loop systems
Comfortable reviewing large-scale datasets across different modalities such as text, audio, images, or video
Care about quality and consistency, and bring a structured approach to evaluating data
Ability to communicate clearly and explain complex ideas in a simple, accessible way
Ability to collaborate well with cross-functional partners in fast-moving environments
Solid understanding of the machine learning lifecycle, from data collection to deployment
Comfortable working with emerging AI tools and agent workflows
Curiosity and interest in music, podcasts, or audiobooks
