Approaching the Hiring of Engineers as a Machine Learning Problem

Hiring software engineers is challenging due to the inherent difficulty in assessing whether candidates are truly "hirable," which involves evaluating their skills, experience, and personality. The process can be likened to a machine learning classification problem where the goal is to sort candidates into "hirable" and "non-hirable" categories based on input features gathered from interviews. These features are derived from candidates' responses to a series of questions, which are often reused, leading to potential biases and inaccuracies. The challenge is compounded by the small size of the training set (previous interview data) and the difficulty in accurately labeling candidates as good or bad hires due to limited feedback on false negatives (rejected candidates who would have succeeded) and false positives (hired candidates who underperform). The high cost of false positives makes interviewers cautious, often resulting in conservative hiring decisions. Despite these challenges, the process benefits from ensemble learning during debrief meetings, where multiple interviewers contribute to the final hiring decision by aggregating their individual assessments.

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