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Description: Judging individual differences of interaction partners is a key mechanism of human social functioning. However, investigating the behavioral underpinnings of these judgments at a larger scale has traditionally been difficult. We present a machine learning-based approach for cue extraction and integration allowing for large-scale, fine-grained behavioral analyses of social judgments and showcase its application for the case of language behavior and judgments of individual differences in performance. We used a natural language processing approach to extract granular verbal and paraverbal language cues from audio streams and transcripts from a high-stakes assessment center (N = 556, C = 20,206 cues). We subsequently leveraged machine learning models to examine how well targets’ language behavior predicted perceivers’ performance judgments. We then analyzed the predictivity of different language domains and identified the cues that drove our predictions. We found that both verbal and paraverbal language behavior predicted AC performance judgments, but a combination of the two domains led to limited improvement in predictive performance. Cue level analyses revealed that the cues from both subdomains that perceivers used express similar information. We discuss contributions to the performance judgment literature as well as implications for future research on judgments of individual differences in general.