The building sector accounts for nearly 40% of global energy consumption and plays a critical role in societal energy security and sustainability. A building energy model (BEM) simulates complex building physics and provides insights into the performance of various energy-saving measures. The analysis based on BEMs has thus become an essential approach to slowing down the process of increasing building energy consumption. The reliability and accuracy of BEMs have a high impact on decision-making. However, how to calibrate a building energy model has remained a challenge. Existing calibrations are often deterministic without uncertainties quantified. In this study, a new automated multi-module calibration platform, BIR-BEM (Bayesian Inference on R for Building Energy Model), is developed using an R programming language for calibrating building energy models. The sensitivity analysis module determines the calibration parameters, and the building energy model is replaced by the developed meta-model module for the Markov Chain Monte Carlo (MCMC) process to save computing time. An application of a high-rise residential building case in a hot and arid climate was demonstrated. The coefficient of variation with a root-mean-square error (CVRMSE) value of the monthly total cooling energy consumption is 13.95%, which satisfies the monthly calibration tolerance of 15% required by ASHRAE Guideline 14.