For the industry-level context see "China-EU EV Consultations Conclude, World AI Summit Opens in Bengaluru", and the enterprise document list is in "A Bilingual Document Checklist for EV Exports". This article looks at the situation from the translator side: if you have two or three years of general translation experience and want to move into EV, AI enterprise, or commercial space work, what exactly do you do? We pulled together what 37 translators have done at a Shenzhen translation company over the past three years and distilled it into three workable entry paths.
Path One: pick up what comes in, build on the job (fastest, lowest barrier)
This is the most common route. A translator working on business or IT documents gets a one-off AI customer support manual assignment. After that client returns with an EV compliance file, then a commercial space operations manual — a termbase forms, domain familiarity accumulates, and suddenly the translator has a track record. The upside is speed; the downside is the starting material is usually not the core documents but the side work of document translation — AI model annotation standards, EV BOM lists with part-name translations — that pay lower rates. But you do not need to be ready before you start. You get ready while you work.
Two traps to avoid: one, do not pick up work across all three domains at the same time. Start with one (say AI enterprise), make that termbase solid, then expand. Terminology bleeds across domains and it is easy to get confused. Two, after every project spend 30 minutes logging new terms with their industry source and client-confirmed renderings — that wordlist is the admission ticket for your next project.
Path Two: study the domain first, enter with preparation (slowest, highest ceiling)
If you already have a track record and do not want to start from the bottom of the pile, this is the route. Pick one domain (commercial space, for example), read two or three entry-level books (Introduction to Aerospace Engineering in both Chinese and English, the NASA aerospace terminology glossary), subscribe to the official RSS feed of the target regulator (the EU Battery Regulation updates, ESA standards), and attend a World AI Summit stream or two online. When you feel ready, contact a translator hub and say "I am prepared to take work in this domain" — you land core projects directly.
This path takes 3 to 6 months of preparation, but the projects you enter are the client's core assets — AI medical imaging clinical reports, satellite insurance policies — that pay 30-50% more than side work. One of our translators spent four months self-studying aerospace fundamentals before approaching us; he is now our lead translator for commercial space projects and earns twice what he did on business translations.
Path Three: start in MTPE editing, convert to full translator (lowest barrier, demanding pace)
MTPE — machine translation post-editing — is the newest entry point. The workflow is straightforward: run the full document set through a domain-termbase-trained engine, then hand it to a translator for polish and proofreading. This is less demanding than pure human translation because you do not build the whole thing from scratch; you identify where the machine went wrong and fix it. Many ask whether MTPE is "lesser" than human translation — it is not. For technical documentation the key capability is spotting domain-specific machine errors quickly, which requires exactly the subject knowledge you would acquire as a full translator.
Translators taking this path typically move to full human translation in 3 to 6 months because MTPE gives you a complete view of the entire workflow — termbase management, quality checks, client feedback — that side-work translators never see. The downside is pace: MTPE tasks usually have deadlines 20% tighter than pure human translation because the machine has already run the first pass.
Special quirks per domain
A few domain-specific requirements worth noting. EV: you need to understand the WLTP (Worldwide Harmonised Light Vehicles Test Procedure) standard because range figures depend on it. AI enterprise: basic prompt engineering knowledge matters because AI model operation manuals are full of related terms. Commercial space: you need to know the NASA and ESA standard renderings — one insurance policy can present three competing English versions of the same term, and you need to pick the correct one.
Project checklists and termbase templates are available on request — contact us.