Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM = 295 µg/m). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r = 0.52-0.95), but overpredicted PM concentrations (normalized root mean square errors, NRMSE = 80-167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM concentrations. These results suggest the low-cost sensors can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m in the hourly PM concentrations when using a sensor-specific smoke correction equation.