Design and execute linguistic test cases to validate Quick's natural language features across diverse inputs, edge cases, and user scenarios. Evaluate model outputs for accuracy, fluency, and relevance by applying linguistic expertise to identify regressions, hallucinations, and quality gaps. Develop and maintain testing guidelines and rubrics that ensure consistent, repeatable evaluation standards across annotators and test cycles. Analyze test results and report findings using Python and SQL to surface patterns, quantify defect rates, and prioritize issues for engineering teams. Collaborate with engineers, scientists, and PMs to define acceptance criteria, triage language-related bugs, and validate fixes before releases Work on high complex annotations and audits as needed Work effectively both independently and as part of a team Uphold a high bar for quality while delivering accurate work at pace Apply strong attention to detail and Critically assess data quality Manage competing tasks and adapt quickly to shifting priorities Take ownership and Exercise sound judgment in escalating Handle highly complex tasks and Scale with growing workloads