Behavioral measurement is the hallmark of research in the field of computational social science. We are witnessing innovative as well as clever use of existing and novel, commercial, or research-grade “sensors” to measure various aspects of human behavior and well-being. Passive sensing, a version of measurement where data is gathered and tracked unobtrusively using pervasive and ubiquitous sensors, is increasingly recognized and utilized in organizational science research. This chapter presents an overview of where passive sensing has been successful in workplace measurement, ranging from assessing worker personality and productivity, to their well-being, and understanding the overall organizational pulse. A range of passive sensing infrastructures are described (e.g., smartphones, wearable devices, social media) and several machine-learning-based predictive approaches are noted in this body of research. The chapter then highlights outstanding challenges as this field matures, which include issues of limited generalizability in computational measurement of workplace behaviors, gaps and limitations of gold standard assessment, model simplicity and sophistication tradeoffs, and, importantly, privacy risks. The chapter concludes with recommendations on important areas that need further or altogether new investments, so as to fully realize the potential of passive sensing technologies in more accurate, actionable, and ethical workplace measurement.