Remote AI Trainer & Evaluator - Flexible Hours

CNTXT AI; New York (NY); On-site; USD 34,440 - 55,104; Part time

Job summary

CNTXT AI is seeking a fully remote, hourly contractor to support AI data and language projects on a flexible, project-based schedule. The role involves content generation, data annotation, LLM evaluation, and localization QA across diverse topics.

Ideal candidates are native speakers of American English born and raised in the United States, with exceptional editorial judgment, attention to detail, and the ability to explain corrections clearly in writing.

Qualifications

• Native speakers of American English born and raised in the United States.
• Excellent editorial judgment in register, tone, punctuation, inclusivity, and cultural nuance, with extreme attention to detail.
• Ability to identify meaning drift, ambiguity, locale inconsistencies, and subtle errors, and to explain corrections clearly in writing.
• Ability to rigorously fact‑check localized content (units, references, names, dates) using reliable sources and consistent reasoning.
• Ability to identify reasoning gaps, methodological errors, and unclear explanations even when language is fluent.
• Reliable, self‑directed, and able to deliver consistent quality with clear communication and responsiveness across time zones.

Responsibilities

• Write prompts and model responses or record voice samples for AI learning across diverse topics.
• Label, classify, and structure documents and content to support AI training datasets.
• Review AI-generated responses for accuracy, reasoning quality, coherence, and cultural/linguistic appropriateness.
• Ensure terminology, tone, cultural nuance, and locale details are accurate across outputs.

Skills

Editorial judgment

Attention to detail

Localization QA

Fact-checking

Cross-time-zone communication

Quality assurance

Job description

CNTXT AI is seeking a fully remote, hourly contractor to support AI data and language projects on a flexible, project-based schedule. The role involves content generation, data annotation, LLM evaluation, and localization QA across diverse topics.

Ideal candidates are native speakers of American English born and raised in the United States, with exceptional editorial judgment, attention to detail, and the ability to explain corrections clearly in writing.