• Review, validate, and improve AI-generated outputs • Identify systemic errors, edge cases, and opportunities to improve model performance • Must be able to review and document with strong quality focus and application of clinical experience to extract key information for medical summaries • Provide structured feedback to QA and Machine Learning teams on model behavior, error patterns, and data quality issues • Identify recurring data issues or annotation inefficiencies and propose workflow or tooling improvements • Meet or exceed defined productivity and quality benchmarks in a high-throughput, AI-assisted production environment • Other duties and projects as assigned