The presence of Salmonella on almonds continues to result in product-related outbreaks and recalls in the United States. In this study, the impact of microbial reduction treatment levels (1 to 5 log CFU) on the risk of human salmonellosis from the consumption of almond kernels in the United States was evaluated. An exposure model, including major steps in almond processing, was used to estimate prevalence and levels of contamination of Salmonella on almonds at the point of consumption. A Salmonella dose-response model and consumption data for almonds in the United States were used to assess risk of illness per serving and per year, quantifying variability and uncertainty separately. A 3-log reduction treatment resulted in a predicted mean risk of illness of two cases per year for almonds consumed as a core product not cooked at home (95% confidence interval [CI], one to four cases), one case per year for almonds consumed as an ingredient not cooked at home (95% CI, one to two cases), and less than one case per year for almonds consumed as an ingredient cooked at home (95% CI, 7 × 10-7 to 3 × 10-6 cases). A minimum 4-log reduction treatment resulted in an estimated mean risk of illness below one case per year in the United States. This study also includes an assessment of the risk of human salmonellosis as a result of an exceptional situation, which results in higher risk estimates compared with the baseline model. The exceptional situations modeled posttreatment resulted in estimates of mean risk that were not significantly affected by treatment level. Sensitivity analysis results showed initial Salmonella contamination level to be the factor with the most impact on risk per serving estimates, given a certain treatment level. The risk assessment also includes a simulation of the events that occurred in 2001. Treatment levels with a minimum 4-log microbial reduction would have been sufficient to prevent the outbreak cases. The uncertainty range in the estimates indicates that additional information is needed to make more precise predictions of this specific outbreak event.