Support daily operational deliverables for multiple task types assigned to you and the team.
Flag error patterns and escalate to team managers with documented examples for resolution.
Report process gaps or Standard Operating Procedures (SOP) issues to your team lead for review.
Identify day-to-day process and operational issues in SOP and tools and suggest changes to unblock operations.
Support peers by answering task-related questions during execution when needed.
A day in the life
No two projects look exactly alike, so your day-to-day will vary depending on the workflow you’re assigned to. That said, most days follow a familiar rhythm. Weekly team huddles are held by the Team Managers to elevate priorities, guideline updates, and quality feedback. You log into the labeling platform and work through your assigned task queue — this could be evaluating AI-generated dialogue, annotating images, reviewing video, or rating audio quality, which are repetitive by nature. Typically, you spend 6–8 hours of screen-based work per shift with scheduled breaks. You are expected to meet daily throughput targets while maintaining a quality score at or above the team threshold. Ambiguous or non-standard tasks can be an opportunity to work through the situation independently before discussing your approach with your team lead or designated point of contact, where applicable, to ensure alignment. Your work is regularly audited by QA auditors, and you will receive documented feedback on accuracy. Calibration sessions take place occasionally, typically after a dip in quality, to realign the team on labeling standards. You will have a 1:1 with your manager once a month to discuss performance metrics and development.
This role involves repetitive, guideline-driven labeling tasks for 6–8 hours per shift on screen and working with a range of data types, which may include sensitive or potentially distressing content. Work is queue-based, metric-tracked (quality score and throughput), and regularly audited by QA reviewers. This is not an AI research or model development role. You will not be designing algorithms or making product decisions.
About the team
Knowledge Fulfillment Engine – Frontier AI Data Services provides high-quality labeled data for machine learning technologies that power Amazon's AI products. Your labeled data feeds directly into AI model training, though you may not always see the final product your work enables, the labeled data your team provides is the engine that drives AI Model development growth for Amazon and our customers.
A degree or related work experience
Advanced proficiency in French language (CEFR C1+)
Language fluency in both spoken and written English (CEFR C1+)
Experience in business writing and demonstrated ability to create reports, proposals, and professional correspondence.
Experience in research, gathering and synthesizing information from multiple sources
Experience using open-source resources for research
Experience in project coordination or management (for support functions teams)
Experience managing stakeholder relationships across departments
Attention to detail in managing tasks and documents, with the ability to adapt to evolving guidelines and specifications
Analytical thinking and structured problem-solving capabilities
Proficiency in Microsoft Office Suite and common business applications.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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