Hiring nowLanguage and multilingual intelligence

Language Localisation Evaluator

Also called AI Language Specialist · Linguistic AI Evaluator

A Language Localisation Evaluator judges whether model output means the right thing, sounds right and is appropriate in a specific locale. It matters because errors that native users spot at once can quietly erode trust in a product.

What they do

The work starts with a locale and a use case. The evaluator reads model output in that language and checks meaning, register, idiom, terminology and tone against local expectations. They write a rubric that reviewers can apply the same way each time. They run review rounds, compare scores and resolve disagreements into an adjudicated set. They separate harmless awkwardness from errors that mislead, offend or cause harm. They look at how tokenisation and training coverage affect weaker languages. They report patterns to the modelling team in plain terms, with examples.

What a strong hire delivers

In the first months, a strong hire delivers a locale-specific rubric that other reviewers can use without hand-holding. They produce an adjudicated evaluation set that the team trusts and reuses. Reviewer agreement is measured and improving. The team has a clear list of consequential error types for that locale, ranked by risk. Engineers know which failures to fix first and can see whether changes helped.

How you can tell they are good

Ask them to take a model output in their language and mark it up. A strong candidate separates literal accuracy from what a local reader would understand. They explain why a rubric item exists, and show where reviewers disagreed and how they settled it. They can say which errors matter and which do not. Be wary of anyone who only talks about fluency or who treats translation quality as one score.

Why it is hard to fill

The title varies from one company to the next, so searching on it finds few people. The skill sits between linguistics, translation and NLP, and most candidates are strong in only one. Native-level judgement in a given locale narrows the pool further, especially for lower-resource languages. Many good people are in academia or localisation teams and have never worked on model evaluation. We often have to assess the mix, not the job title.

Where to find them

Look at computational linguists, NLP researchers and language technology specialists. Translators and localisation leads who have moved into quality and terminology work are also strong. Some come from machine translation teams, speech and search groups, or university language labs. Multilingual product teams at large platforms, localisation vendors and research groups working on under-served languages are all worth approaching.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Language Localisation Evaluator

  1. Tell us about a locale-specific rubric you built. How did you decide what to include?

    A strong answer names the locale, the use case and the errors that mattered, and explains how the rubric was tested with other reviewers. Worry if the rubric is generic, copied from another language or never checked for agreement.

  2. How do you measure and improve reviewer agreement?

    Good answers cover calibration rounds, clear examples, measured agreement, and adjudication of disagreements. They also note that low agreement can signal a vague rubric, not a poor reviewer. Worry if they only average scores.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring a Language Localisation Evaluator to judge whether our models handle meaning, usage and tone correctly in specific locales. You will build the rubrics and evaluation sets that tell us where the model fails local users, and work with modelling teams to fix it.

What you will do

  • Review model output for local meaning, usage and appropriateness
  • Write and maintain locale-specific evaluation rubrics
  • Run reviewer rounds and measure agreement
  • Adjudicate disagreements into a trusted evaluation set
  • Identify and rank consequential errors by risk
  • Report findings and examples to modelling and product teams

What you will bring

  • Native or near-native judgement in at least one non-English locale
  • Evidence of rubrics or evaluation sets you have built and used
  • Working knowledge of terminology review and cultural pragmatics
  • Familiarity with how multilingual models and tokenisers behave
  • Clear written explanation of language issues to technical colleagues

Nice to have

  • Experience with lower-resource languages or regional variants
  • Background in machine translation or computational linguistics
  • Experience working with multilingual evaluation corpora

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Transformer modellingAdvancedImplement attention-based sequence or multimodal models
  • Representation learningAdvancedLearn transferable features or embeddings
  • Linguistics and localisationWorkingAdapt language behaviour across cultural and linguistic contexts
  • Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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