Because machine translation (MT) still falls short of human parity, human intervention is needed to ensure quality translation. The existing literature indicates that machine translation post-editing (MTPE) generally enhances translation productivity, but the question of quality remains for domain-specific texts (e.g. Aranberri et al., 2014; Jia et al., 2022; Kim et al., 2019; Lee, 2021a,b). Although legal translation is considered as one of the most complex specialist transla-tion domains, because of the demand surge for legal translation, MT has been utilized to some extent for documents of less importance (Roberts, 2022). Given that little research has examined the productivity and quality of MT and MTPE in Korean-English legal translation, we sought to examine the productivity and quality of MT and MTPE of Korean of statutes, using DeepL, a neural machine translation engine which has recently started the Korean language service. This paper presents the preliminary findings from a research project that investigated DeepL MT qua-lity and the quality and productivity of MTPE outputs and human translations by seven professional translators.