This study aims to examine how job resources, demands, and self-efficacy affect American STEM teachers' job satisfaction by analyzing the US TALIS 2018 data. Multiple regression and commonality analysis were used to analyze factors' significant contributions and their detailed real unique and common contributions to STEM teachers' job satisfaction. The results show that the final model explains 29.6% of the variances of STEM teachers' job satisfaction. The commonality analysis further showed that job resources, job demands, and job self-efficacy explained 23.5%, 8.6%, and 8.0% of variances of job satisfaction, respectively. However, these factor sets uniquely contributed 15.9%, 2.9%, and 2.1% of the variance, separately. This study confirms the validity of the revised job demands-resources model for STEM teachers' job satisfaction. Furthermore, the commonality analysis reveals the unique and independent contributions of job demands, resources, and self-efficacy to job satisfaction. Results from the research identified the significance of job resources contributing to the improvement of STEM teachers' job satisfaction.