Machine Translation Post-Editor Job Description

A Machine Translation Post-Editor takes text that a machine produced, whether from a neural engine such as DeepL or Google Translate or from a large language model prompted to translate, and edits it until it meets a quality level the client agreed to in advance. The starting point is the defining feature of the job. A conventional translator begins with a blank target segment; a post-editor begins with a full draft and decides, segment by segment, what to keep, what to fix and what to throw out and retranslate. The Bureau of Labor Statistics describes the step plainly in its profile of translators: machine translation software generates text in the target language, "which translators then review in a process called post-editing."

The work splits into two service levels, and knowing which one a job calls for is half the skill. Light post-editing aims for text that is accurate and understandable: the meaning must be right, numbers and names must be correct, and nothing offensive or misleading can survive, but awkward phrasing that does no harm is left alone. Full post-editing aims for output a reader cannot tell apart from careful human translation, so style, register, terminology and flow all get attention. ISO 18587:2017, the international standard for this work, sets requirements for "full, human post-editing of machine translation output" and for the competences of the people doing it. Overediting a light job wastes the client's budget; underediting a full job ships errors.

The day's work happens inside a computer-assisted translation (CAT) environment such as Trados Studio, memoQ or Phrase. The machine draft arrives pre-filled in each segment alongside matches from the translation memory and hits from the termbase. The post-editor works through the file against a client style guide and glossary, flags source-text problems back to the project manager, and runs automated QA checks for numbers, tags, untranslated segments, punctuation and terminology consistency before delivery. On some accounts the edits themselves become data: the difference between the raw machine output and the final version is logged, and reviewers may score samples against an error typology such as MQM, which sorts problems into categories like accuracy, fluency, terminology and style with a severity for each.

The content is varied: software strings and help pages, e-commerce product listings, customer support articles, user reviews, technical manuals, internal corporate communications, and, under tighter rules, patient-facing health material and legal or financial documents. The audience and the risk of the text decide the level, and good post-editors push back when a client orders light post-editing on content that cannot tolerate an error.

The people around the role are mostly on the localization side of a business: project managers at language service providers who assign files and deadlines, in-house localization managers at software and retail companies, terminologists who maintain glossaries, second-pass reviewers, and the engineers who train or configure the machine translation engines and want structured feedback on recurring errors. A post-editor who can describe an engine's failure patterns clearly, such as a consistently mistranslated product term or a gender agreement error in one language pair, is giving those engineers something they can fix.

A post-editor is a translator first. The job cannot be done without full professional command of the source language and native or near-native writing ability in the target language, because the core task is spotting the places where fluent machine output says something the source does not. Fluent text that is wrong is the hardest error to catch, and only someone who reads the source closely will find it.

As a rough guide, the typical path is a bachelor's degree in translation, a foreign language, linguistics or a subject field such as engineering, law or life sciences, paired with experience translating in that field. Graduate programs in translation and localization management are another route, and some of them cover machine translation workflows and post-editing. A subject specialty matters as much as the language pair: a post-editor working on clinical trial documents needs to know the medical terminology, and one working on software strings needs to understand variables, placeholders and character limits.

Credentials are optional but useful for showing competence to clients. The American Translators Association awards ATA certification, earned by passing a language-pair-specific exam, and the credential signals translation skill that carries straight into post-editing. Vendor training from CAT tool makers, such as Trados certification from RWS, can help a candidate show tool fluency, though it is no substitute for language skill.

Technical comfort is the second half of the profile. Post-editors spend their days in CAT tools, so skills such as handling tagged files, filtering segments, running QA checks, applying regular expressions in search and replace, and managing translation memories and termbases count. Familiarity with how neural engines and large language models fail helps too: dropped negations, hallucinated content, inconsistent terminology across a document, wrong units or number formats, and confident mistranslation of rare terms.

As a rough guide, 2 to 5 years of professional translation is a sound foundation for dedicated post-editing work, though a junior linguist can start on light post-editing projects. Speed matters because post-editing can be priced by word or by hour against a throughput target, but speed without accuracy is a liability.

Soft skills are concrete in this role. Post-editors need judgment about when to retranslate a segment from scratch rather than patch it, discipline to stop at the agreed quality level, and the confidence to tell a project manager that a file ordered as light post-editing actually needs full treatment. Clear written feedback to engine owners and reviewers rounds out the profile.

The federal data do not track post-editors as a separate group, so the outlook has to be read through the parent occupation. The Bureau of Labor Statistics projects that employment of interpreters and translators will grow 2 percent from 2025 to 2035, slower than the average for all occupations, with about 6,000 openings a year on average over the decade, most of them expected to result from the need to replace workers who transfer to different occupations or exit the labor force. BLS counted 73,900 jobs in the occupation in 2025. The largest employment groups were professional, scientific and technical services at 30 percent, self-employed workers at 28 percent, and educational services at 15 percent.

BLS is direct about technology in its outlook: computer tools, including artificial intelligence, "are making the work of translators and localization specialists more efficient," but many of these jobs "cannot be entirely automated because computers cannot yet produce work comparable to what human translators do in most cases." The same BLS profile already names post-editing as part of the translator's job.

International standards address the work directly. ISO 18587:2017 was written for machine translation output. Its replacement, ISO/DIS 18587 edition 2, carries a new title, "Post-editing of non-human translation output," and covers "content generated by non-human translation systems," a wording broader than the 2017 edition's "content processed by MT systems." As of September 2026, the iso.org listing showed the draft in its DIS ballot and enquiry phase with ISO members, so it was not yet a published standard. Alongside it, ISO 5060:2024 gives guidance on evaluating "human translation output, post-edited machine translation output, and unedited machine translation output" using error types and penalty points. For a post-editor, that means quality can be measured and documented against a published method, and comfort with structured evaluation can be part of the job.

Regulation sets a floor for human review in at least one sector. Under the federal rule on nondiscrimination in health programs and activities, 45 CFR 92.201(c)(3) says that if a covered entity uses machine translation when the text is critical to the rights, benefits or meaningful access of a person with limited English proficiency, when accuracy is essential, or when the material contains complex, non-literal or technical language, "the translation must be reviewed by a qualified human translator." That text was in the eCFR when this page was checked in November 2026. Covered hospitals, insurers and clinics that machine-translate that kind of material therefore need a qualified human translator to review it, which is post-editing work by another name.

Career paths branch in a few directions. Strong post-editors can move into review and quality lead roles that set and audit quality levels, into terminology management, into vendor management at a language service provider, or toward the technical side, configuring engines and prompts and building the feedback loops that improve them. Specializing in a regulated domain such as healthcare, life sciences, legal or finance, where human review is required or expected, puts a post-editor in fields where, in the American Translators Association's words, "no one can afford the risks posed by AI-generated mistranslations."

Dear Localization Team,

I am applying for the Machine Translation Post-Editor position on your Spanish team. For the past four years I have worked as a freelance English to Spanish translator focused on software and medical device documentation, and for the last two of those years about half of my volume has been machine translation post-editing delivered through Trados Studio and Phrase.

What I bring to post-editing is discipline about the quality level. On light post-editing jobs for a consumer electronics client, I fix meaning, numbers, product names and anything that could mislead a buyer, and I leave harmless phrasing alone so the client gets the speed it paid for. On full post-editing for an instructions-for-use project, I treat the machine draft as a suggestion and edit until the text reads as if a specialist wrote it in Spanish, checking every measurement, warning and cross-reference against the source.

I have also learned to make my edits useful beyond the file in front of me. On one engagement I kept a running log of recurring engine errors, including a battery term the engine translated three different ways and dropped negations in warning sentences. I sent the log to the project manager with suggested glossary entries, and the engine owner added the terms to the termbase, which cut the same corrections out of later batches.

I hold ATA certification for English into Spanish, I am comfortable with tagged XML and software strings, and I run QA checks before every delivery rather than relying on a reviewer to catch what I missed. I would welcome the chance to complete a test file for your team.

Thank you for your consideration.

Sincerely, Daniela Ortega