Spotify - Annotation QA Analyst
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Requirements
• You have experience working with annotation, data quality, or QA processes in ML/AI environments • You’re familiar with LLM or AI-driven annotation workflows and human-in-the-loop systems • You’re comfortable reviewing large-scale datasets across different modalities such as text, audio, images, or video • You care about quality and consistency, and bring a structured approach to evaluating data • You communicate clearly and can explain complex ideas in a simple, accessible way • You collaborate well with cross-functional partners in fast-moving environments • You have a solid understanding of the machine learning lifecycle, from data collection to deployment • You’re comfortable working with emerging AI tools and agent workflows • You bring curiosity and interest in music, podcasts, or audiobooks • You have familiarity with SQL or music metadata standards (nice to have) • Where You'll Be • This role is based in New York. • 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. • The United States base range for this position is $82 921,00 - 118 459,00 plus equity. 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. These ranges may be modified in the future. • At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
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
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