• 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 and compare data with source documents to detect clinical or technical errors • Provide structured feedback to QA and Machine Learning teams on model behavior, error patterns, and data quality issues to directly influence training and iteration cycles • 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