About the Project
We’re searching for talented linguists to join Project Hamal and help us redefine how travelers engage with car rental and driving tips worldwide. This isn’t just a translation job — it’s an opportunity to craft AI-powered localization prompts that produce natural, culturally relevant, and engaging content for a major travel platform. You’ll play a key role in ensuring travelers get clear, concise, and actionable advice, all while amplifying the convenience of car hire services globally.
What You’ll Be Doing
Engineer system and user prompts that guide LLMs (large language models) to create fluent, native-sounding content for Chinese (Traditional)
Localize car hire tips across six key topics: driving style, parking, tolls and road charges, getting to rental counters, top sights by car, and insurance.
Chinese (Traditional) -specific rules to ensure prompts account for grammar, formality, pronoun conventions, transport terminology, and cultural nuances.
Produce two localized example outputs based on real source content.
Identify and flag edge cases to help the team handle complexities like language expansion, cultural sensitivities, or technical constraints.
Project Details:
Start Date: ASAP
Duration: up to 5 hours with possible extension
Job Type: Freelance
Location: Remote (Taiwan)
Language: Chinese (Traditional)
Pay Rate: $29.00/hour
What We’re Looking For
Native or near native speaker of the Chinese (Traditional) language with strong English proficiency.
Proven experience in localization or UX writing — you know how to work within tight character limits and craft user-friendly content.
Confidence in AI content workflows — you can structure, refine, and evaluate LLM prompts to ensure high-quality outputs.
Familiarity with travel and transport terminology in your market — you understand car hire, tolls, parking systems, and driving regulations.
Nice to Have
Experience writing for travel platforms or mobile UX environments.
Knowledge of local driving laws, emissions zones, or toll systems.
A background in evaluating AI-generated text for fluency and tone.