Team Lead (Greek Language QA)

You will lead quality checks for Greek AI training content and trainer QA submissions. You will assess accuracy, fluency, grammar, spelling, tone, cultural fit, meaning, instruction-following, formatting, and rubric compliance.

You will also help keep project standards consistent across contributors and improve workflows for Greek-language projects.

• Review Greek AI training content and trainer QA submissions.
• Write clear feedback and escalate recurring or critical issues.
• Share project guidelines, updates, and expectations with trainers and QAs.
• Support contributor onboarding and training calls.
• Maintain style guides, trackers, FAQs, examples, honeypots, and onboarding materials.
• Identify workflow gaps and suggest process improvements.
• Help activate contributors who are not working consistently.

This is an intermediate, part-time contractor role for someone with strong Greek language-review skills and experience supporting remote contributor teams.

• Pay: $30 per hour.
• Time: 20+ hours per week.
• Location: Open to candidates in Greece.
• Language: Fluent Greek with strong reading and writing skills, plus strong English communication.
• Experience: Greek writing, editing, translation, localization, content QA, AI training, annotation, or a related language-review workflow.
• Knowledge: Greek grammar, spelling, punctuation, tone, register, and cultural context, including differences between Greece and Cyprus usage.
• Team experience: Supporting trainers, annotators, reviewers, editors, or QAs on remote teams.
• Helpful education: Greek, Linguistics, Translation, Communications, Journalism, English, Education, or Quality Assurance.
• Helpful experience: AI training, large language models, prompt or response evaluation, or rubric-based LLM QA.
• Helpful tools: Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind systems that understand and generate text. People review examples, rate model responses, and check language quality so AI systems can improve, and experienced contributors are needed for work that requires careful judgment and subject knowledge.