Canadian English Linguistic Expert

$40/hr

rate as listed

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

Evaluate and improve LLM outputs for grammatical accuracy, naturalness, and cultural fit in Canadian English. The work includes editing, linguistic annotation, error analysis, and detailed feedback for AI researchers.

What the work involves

You will review LLM-generated Canadian English across translation, summarization, dialogue, and grammar-correction tasks. Day-to-day work may include editing or rewriting outputs, annotating grammatical, syntactic, and semantic features, identifying recurring model errors, and explaining how an output should be improved.

What the screening focuses on

Mercor is looking for native or near-native fluency in Canadian English, supported by professional experience in translation, editing, linguistic annotation, or NLP data work in English. Screening is likely to test whether you can distinguish Canadian usage from American and British English, assess register and regional variation, and turn subtle linguistic judgments into consistent, actionable feedback. Interest in AI and LLM-driven language technologies is preferred but not required.

Practical details

The role is remote and designed for work with a distributed team. The listing does not disclose typical weekly hours, scheduling requirements, or whether all work is asynchronous, so candidates should be prepared to state their availability and time-zone constraints clearly.

Pay band: $40/hr

What they look for

Native or near-native fluency in Canadian English
Professional experience in translation, editing, linguistic annotation, or NLP data work in English
Knowledge of Canadian English grammar, syntax, stylistics, register, and regional variation
Ability to distinguish Canadian English from American and British English usage
Linguistic annotation and structured LLM error analysis
Experience working in distributed, remote teams

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 the specific text-evaluation or data-curation workflows named in the role, rather than general writing experience alone.
A strong answer: Give a concise chronology with project types, your exact responsibilities, the kinds of English data you handled, and one or two concrete deliverables such as edited corpora, annotation batches, or quality reviews.
Where people lose it: Candidates lose points by describing broad writing or teaching experience without showing that they have evaluated, transformed, or annotated language data under defined standards.

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