Laboratory Engineer & Researcher (Applied research and educator-style evaluation)
Conducted AI training–adjacent evaluation and quality assessment by benchmarking student project outputs against engineering criteria and rubrics. Translated complex STEM expectations into consistent scoring behavior that mirrors evaluation loops used in AI training workflows. Supported iterative feedback cycles through structured guidance to improve technical responses and outputs. • Assessed engineering projects using rubric-based scoring • Performed quality evaluation analogous to rating/validation for AI outputs • Provided iterative feedback to refine responses and results • Ensured benchmark alignment for consistent performance measurement