Recent advances in machine learning have made automated scoring of learner speech widespread, and yet validation research that provides support for applying automated scoring technology to assessment is still in its infancy. Both the educational measurement and language assessment communities have called for greater transparency in describing scoring algorithms and research evidence about the reliability of automated scoring. This paper reports on a study that investigated the reliability of an automarker using candidate responses produced in an online oral English test. Based on 'limits of agreement' and multi-faceted Rasch analyses on automarker scores and individual examiner scores, the study found that the automarker, while exhibiting excellent internal consistency, was slightly more lenient than examiner fair average scores, particularly for low-proficiency speakers. Additionally, it was found that an automarker uncertainty measure termed Language Quality, which indicates the confidence of speech recognition, was useful for predicting automarker reliability and flagging abnormal speech.