Russian Quality Assurance Lead (Remote Contractor)

About the role

This remote, hourly contractor position focuses on leading quality assurance for Russian AI training projects. You will oversee the consistency and performance of trainers and QAs, review AI‑generated Russian content, and ensure that all contributors meet strict quality standards.

Key responsibilities

• Spot‑check Russian items, identify quality issues, provide feedback, and escalate critical problems.
• Communicate updates, guidelines, and workflow changes to trainers and QAs via Discord.
• Answer trainer/QA questions on Russian wording, grammar, cultural context, and terminology.
• Activate and monitor inactive contributors, track follow‑ups, and flag availability concerns.
• Create and maintain project documentation such as style guides, FAQs, trackers, and onboarding materials.
• Conduct onboarding and training sessions to explain rubrics, quality standards, and Russian‑specific style requirements.
• Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes.

Required profile

• Bachelor’s or Master’s degree in Russian, Linguistics, Translation, Communications, Journalism, English, Education, Quality Assurance, or a related field.
• Native or near‑native Russian proficiency with strong reading and writing skills.
• Strong English skills for following guidelines and providing feedback.
• 3+ years of professional experience in Russian writing, editing, translation, localization, content QA, AI training, or related language‑review workflows.
• Deep knowledge of Russian grammar, spelling, punctuation, declension, aspect, gender agreement, tone, register, and cultural context.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, or QAs is strongly preferred.
• Comfortable using Discord, Google Sheets, Google Docs, trackers, dashboards, and project‑management tools.

Required skills

• Discord for team communication.
• Google Sheets and Google Docs for documentation and tracking.
• Project‑management and dashboard tools.
• AI training and data annotation workflows.
• Large language model evaluation, prompt/response assessment, and rubric‑based QA.