English Linguistic Experts

$50/hr

rate as listed

Workload: ~15h/week
Engagement: Project-based
Location: Worldwide
Field: Languages

Evaluate, annotate, edit and rewrite English text used to assess and improve large language models. The work calls for native or near-native English fluency and professional experience in translation, editing, linguistic annotation or NLP data work.

The work

You will assess LLM-generated English across tasks such as translation, summarization, dialogue and grammar correction. Day to day, this may involve identifying grammatical, syntactic and semantic errors, rewriting unnatural passages, annotating linguistic features and documenting gaps in model performance.

What the platform screens for

Mercor is looking for native or near-native fluency in English, backed by professional experience in translation, editing, linguistic annotation or NLP data work in English. Screening is likely to test whether you can explain subtle judgments about grammar, style, regional variation, cultural fit and context, then apply those judgments consistently when examples become ambiguous. Interest in AI and LLM-driven language technologies is preferred but not required.

Practical details

The project is designed for distributed, remote collaboration with AI researchers. The listing does not state whether work is fully asynchronous, how many hours are typically available or the expected project duration, so candidates should confirm scheduling, workload and turnaround expectations during onboarding.

Pay band: $50/hr

What they look for

Native or near-native English fluency
Professional English translation, editing, linguistic annotation or NLP data experience
Advanced knowledge of English grammar, syntax and stylistics
Recognition of regional variation, linguistic nuance and context-appropriate usage
Detailed analysis of LLM errors and unnatural phrasing
Clear written feedback in a distributed remote team

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This establishes whether your background includes hands-on English language evaluation rather than only general writing or informal proofreading.
A strong answer: Give specific roles or projects, the types of text handled, the judgments you made and the volume or duration of the work. Distinguish clearly between translation, editorial, annotation and NLP responsibilities.
Where people lose it: Candidates lose points by describing themselves as strong writers without naming a professional workflow, deliverable or quality standard.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.