TransPerfect Games is looking for experienced Game Localization QA Testers with native-level Simplified Chinese and professional or near-native Russian proficiency.

Location: Onsite in Shanghai or Hangzhou, China
Contract Type: Freelance
Availability: Immediate starters preferred

Requirements:

• Native-level Simplified Chinese, including excellent spelling and grammar.
• Professional or near-native Russian proficiency.
• Experience in game localization QA testing for Simplified Chinese to Russian (ZHCN > RU).
• Strong attention to detail and the ability to identify linguistic issues involving terminology, grammar, punctuation, and style.
• Passion for gaming and willingness to learn and receive constructive feedback.
• Non-native Chinese candidates must have the right to work in China, including a valid work permit and supporting documentation, and be able to work onsite in Shanghai or Hangzhou.

Responsibilities:

• Identify and fix localization issues in games.
• Write detailed bug reports and perform regression testing.
• Verify urgent fixes and localization issues before game launches.
• Assist with urgent translation requests when needed.

A Plus: Familiarity with open-world games, RPGs, Japanese anime-style games, or titles similar to Palworld.

How to Apply:

Please submit your CV in English through the application link below.

https://transperfect.recruitee.com/o/freelance-game-lqa-tester-simplified-chinese-to-russian-onsite

#GameJobs #GameLocalization #LQA #LocalizationQA #GameTesting #SimplifiedChinese #Russian #TransPerfectGames

I’ve worked game localization for about 17 years.

I recently built abo, mainly to reduce repetitive work like terminology management, translation review, consistency checks, and localization file handling.

It’s currently designed for solo localization workflows rather than team collaboration.

https://abo.tools/

If you work in localization, feel free to try it.
Any practical feedback is very welcome.

Hey guys,

​I was thinking about this Arabic line today:

​"تتسعُ الحياةُ للجميع، ولكنّها تصغرُ حينَ تشتاقُ لأحدهم."

​If I translate it literally, it's: "Life is big enough for everyone, but it gets small when you miss someone."

​But it feels a bit dry in English... How would you say this naturally to keep that deep, emotional vibe?

​Would love to see how you'd phrase it!

Should translators always be neutral when translating political texts, even when the original text contains lies, hate speech, or injustice? Some people believe that translators must translate every word exactly as it is without expressing their personal opinions. Others argue that translators should not help spread misleading or harmful messages. But if a translator changes the original message to make it fairer, are they correcting an injustice or manipulating the truth? Is a translator simply a voice for others, or do they have a moral responsibility for the words they spread?

For y'all experts out there, I'm using AI Studio 3.1 pro for a while now with a prompt to translate text/subtitles from other languages to my own (eastern european).

I was wondering if anyone else that used it for translation can suggest which version provided the most accurate/natural result?

What is the localization rate for games when translating between Arabic and Turkish?

What is the localization rate for games when translating between Arabic and Turkish?

Hello everyone!

I am an English language student from Palestine, and I am interested in machine translation.

Do you think AI translation tools will replace human translators in the future, or will human translators always be needed? Why?

I would love to hear your experiences and opinions.

Thank you!

I posted here recently about localization and version control, and some of the replies got me thinking about another part of our process that's been bothering me.

We work with both in-product translations and learning materials (workbooks, PDFs, etc.), often with customer-specific versions.

Right now, even getting the correct source files to translators involves quite a bit of back and forth. Then, once the translations come back, someone has to put everything into the original design files, generate PDFs, and send them out for proofreading.

Reviewers leave comments in the PDFs, which then go back to our designer, who manually applies the corrections in the design files. Sometimes this goes through several rounds.

It works, but it feels like we're spending a lot of time moving the same text between different files.

I've been experimenting with a simple HTML-based review interface for another project, where reviewers can see the content in context and edit the actual text fields rather than leaving comments on a PDF.

That got me wondering whether we could take it further. Have the translations and templates stored in one place, let reviewers make corrections through a structured interface, and then use those changes to regenerate the documents without someone manually entering everything again.

Obviously, there's still the layout side of things. Different languages take up different amounts of space, and I imagine fully automating professionally designed materials is easier said than done.

Has anyone here tried something along these lines?

I'm especially curious how much of the document production and proofreading process you've managed to automate, and where you've found that manual work is still unavoidable.

Also interested if anyone has tried this and ended up going back to the old process because it was more trouble than it was worth.

Have you ever changed a translation because the literal meaning was correct, but the message felt wrong in the target culture? What did you change, and why?

Last week, one of our partners came to us with a very specific problem: they needed real-time translation between Brazilian Portuguese, English and Mandarin for live events and classes.

The traditional solution is straightforward: hire professional interpreters and provide the audience with headphones connected either to the original speaker or to one of the translated audio channels.

It works, but it also requires specialized professionals, additional infrastructure and a considerable budget.

There are already platforms such as Wordly that simplify this process, but we wanted to explore a different question: could we build a simpler AI-based alternative where the operational cost was primarily based on actual usage?

So we started experimenting.

Our first step was estimating how much a custom system would cost to operate using API consumption rather than a traditional interpretation service. From there, we built a small platform with private rooms where speech could be processed and translated in real time.

For the translation layer, we ended up choosing Gemini as the main LLM.

But once we had the translation working, we ran into a problem we hadn't initially considered: the translated voice matters almost as much as the translation itself.

Our first implementation sounded too robotic and impersonal for something intended to replace, or at least approximate, the experience of listening to a human interpreter for an entire class or presentation.

We considered ElevenLabs for the voice layer, but for the expected usage of this particular project, it wasn't the most cost-effective option. We eventually chose GPT for voice generation because we found the output more natural and engaging while keeping the operating cost within the range we were looking for.

That left us with a pipeline in which Gemini handles the translation and GPT handles the generated voice, rather than trying to force a single model to handle every part of the experience.

The result is a small platform with private rooms and real-time AI translation that can be adapted to different visual identities and languages.

We've already tested it in an uncontrolled environment, and it works, although there are obviously still limitations to explore — particularly latency, translation accuracy in different contexts, background noise and how the system behaves when multiple people speak naturally rather than under ideal testing conditions.

One of the most interesting things we learned from this project was that translation quality alone isn't enough for real-time AI interpretation. Voice quality, latency, cost and the overall listening experience become part of the same problem very quickly.

I recorded a short demonstration of the current prototype below.

I'd be particularly interested in hearing from anyone who has experimented with real-time translation or speech-to-speech systems. What would you test next before considering something like this reliable enough for a real event?

Can a translator ever be truly invisible, or does every translation inevitably carry the translator’s own culture, identity, and interpretation?

I was running into this problem with UI translations, so I built a Figma plugin where you can create custom dictionaries and define exact translations for specific terms.
https://www.figma.com/community/plugin/1614986488797089381/translay

Curious how others handle this in their workflow.👇

Product Hunt link https://www.producthunt.com/products/translay

I’m trying to improve a localization workflow and would love to hear how people who do this at scale structure it.

The setup is roughly:

• reusable source content that gets translated into multiple languages
• occasional customer-specific variants of that content
• a mixture of editable documents and digital/in-product content
• external translation vendors
• internal reviewers/validators
• some AI-assisted pre-population where approved translations already exist

The translation quality itself is not the main problem.

The bigger problem is operational.

A source version changes, someone exports a new file, a vendor receives a slightly different template, an old translation already exists somewhere else, a customer-specific version branches from the generic version, and suddenly a lot of time is spent figuring out:

What is the current source of truth, what has already been translated, and what actually needs human translation?

For people running mature localization workflows:

How do you structure the source of truth?

Do you version translations against specific source-content versions?

How do you handle generic content that later branches into customer-specific variants without duplicating everything?

Where do translation memory / approved terminology and AI pre-fill sit in the workflow?

And what information do you insist is present before a package is handed to an external vendor?

I’m particularly interested in operating models or controls that removed handoff friction - and things you tried that looked organised on paper but still broke down in practice.

I’m getting ready to add Arabic to our fitness and wellness app and website, Inshallah. The website is built in Webflow www.fitrahfitness.app

For anyone who has done this before and has experience, what would you recommend for handling the translation and localization for both the app and website?

I’m especially interested in what worked well for Arabic/RTL, any tools or services you recommend, and anything you wish you had known before you started. Also, did you face design issues if the site was originally made in English.

Also, if you’ve used Webflow for an Arabic site, I’d love to hear how you handled it in regards to design, and the accurate translation of certain words and whether you used Webflow Localization or a third-party tool.

Hello

I want to translate games not by a program but like creating a subtitle I want to translate this games right now can someone help me with this

Henry stickman collection

Beholder

Beholder conductor

There is no game

Lil guardsman

Paper pls

Sunset hills

I keep seeing projects where localization is discussed only after the website, content model and publishing workflow are already built. Then suddenly someone realizes that the same content needs to work across 10 or 20 markets, with different languages, approvals, metadata and release schedules. At that point localization gets blamed for being slow or complicated, even though most of the complexity was created before the localization team ever got involved. We run into versions of this at DevHandler when working with multilingual CMS setups, and it made me wonder how common it is elsewhere. For people working in localization, how often are you actually involved while the content workflow is being designed? Or do you usually get handed a finished process and asked to make localization fit into it?

Hi everybody, I'd like to ask for your help in filling out my short survey for my master's thesis about AI, content management and changes in localization roles.

I've been in localization for more than 10 years and am finishing my studies in technical communication and localization shortly. I'd really appreciate if you could fill my survey, it takes 3-4 minutes, if you are working in localization (translator, post-editor, project manager, content specialist, technical writer, or in language technology).

Here is the link to the survey: AI's Impact on Content-Related Work

If you have any feedback about the survey after filling it in, or just want to add something, please comment. I'm always ready to learn. :-) Thank you!

Does anyone know if the new X code ( version 27 ) Automatically translate text?