This paper examines a human–ML framework for large-scale qualitative data coding. Using a nationally representative sample of transcript data, the study evaluates semantic embeddings and ranked recommendations through validation and user testing. Results demonstrate improved efficiency and accuracy while maintaining human expertise, oversight, and responsibility for final coding decisions.