• Establish and evolve a reliable clinical QA and adjudication workflow across evaluation projects, identifying gaps and improving on systems • Train and onboard expert raters on safety rubrics, shared standards, and evaluation philosophy • Run calibration sessions to reduce disagreement and improve consistency • Serve as the escalation point for ambiguous or high-risk conversations • Deliver adjudicated, high-confidence evaluation datasets for an initial pilot • Demonstrate calibrated clinical judgment aligned with mpathic’s safety philosophy • Design workflows that others can run without constant oversight • Own clinical quality systems across multiple concurrent AI safety evaluation programs • Develop scalable training, certification, and feedback loops for expert evaluators • Continuously improve inter-rater reliability, rubric clarity, and reviewer consistency • Build gold-standard examples and clinical playbooks for sensitive user contexts • Identify recurring model failure patterns and inform rubric/tooling improvements