Translation memory (TM) and machine translation (MT) were, until quite recently, considered to be distinct and diverging technologies. The current trend in translation technology, however, is to attempt to create a synergy of the two. At present, the TM tools used for the last decades to recycle human translation are being adopted also for the task of post-editing MT output. We consider that while these existing translation editor interfaces are by now familiar and functional for translators working with TM, any support for post-editing or integration with MT has tended to be appended as an afterthought. Post-editing of MT output is different from revision of an existing translation suggested by a TM – or, indeed, from translation without any suggestion whatsoever – primarily because the types of revision differ. Machine translation output tends to include mistakes that professional human translators would not generally make. When this is coupled with the fact that few professional translators have received training either in MT technology or in post-editing practices to date, the result is often apprehension among translators with regard to the post-editing task, along with a high level of frustration. Some of the most common complaints from translators about the task of post-editing stem from the fact that it is an edit-intensive mechanical task that requires correction of basic linguistic errors over and over again (Guerberof 2013; Moorkens and O’Brien 2014). Understandably, translators see this task as boring and demeaning, and especially despise it when the ‘machine’ does not ‘learn’ from its mistakes or from translators’ edits. Kelly (2014) even goes so far as to call this task ‘linguistic janitorial work’.