Ukrainian Team Lead
Up to $30/hr as listed
- Listed pay
- Up to $30/hr as listed
- Field
- Languages
- Languages
- Ukrainian, English
- Where
- Ukraine, Canada, Poland, United States, Kazakhstan, Moldova
- Type
- Contract, remote
- Posted on SME Careers
- First seen here
- 9 October 2026
Summary
In this hourly, remote contractor role, you will work as a Ukrainian Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Ukrainian AI training projects...
From the SME Careers listing
In this hourly, remote contractor role, you will work as a Ukrainian Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Ukrainian AI training projects. You will review AI-generated Ukrainian content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.
You will assess work for accuracy, fluency, grammar, spelling, tone, cultural appropriateness, meaning preservation, instruction-following, formatting, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong Ukrainian and English skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote teams.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your Ukrainian quality leadership will directly help improve the world’s premier AI models by ensuring that Ukrainian training data is natural, accurate, culturally appropriate, well-documented, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.
Responsibilities
- Quality monitoring: Spot-check Ukrainian items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, and quality expectations.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around Ukrainian wording, grammar, register, translation fidelity, cultural context, terminology, and edge cases.
- Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
- Documentation: Create and maintain Ukrainian project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, and onboarding materials.
- Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and Ukrainian-specific style requirements.
- Quality alignment: Ensure all trainers and QAs apply Ukrainian language guidelines consistently and understand updates as projects evolve.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for Ukrainian-language projects.
Requirements
- Bachelor’s or Master’s degree in Ukrainian, Linguistics, Translation, Communications, Journalism, English, Education, Quality Assurance, or a relevant domain/related field.
- Native or near-native Ukrainian proficiency with strong reading and writing skills.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear feedback in English.
- 3+ years of professional experience in Ukrainian writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review workflows.
- Strong understanding of Ukrainian grammar, spelling conventions, punctuation, declension, gender agreement, tone, register, and cultural context.
- Ability to evaluate Ukrainian content against detailed rubrics and identify issues such as mistranslation, literal phrasing, unnatural tone, incorrect case usage, hallucinated claims, ambiguity, or inconsistent terminology.
- Comfortable handling formal, neutral, and conversational Ukrainian, including audience adaptation, localization terminology, and terminology consistency across domains.
- Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
- Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
- Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, and other quality documentation.
- Experience with AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a strong plus.
Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.
Prepare for the assessment
Practice questions and what each platform says about its screening for this kind of role:
Terms in this listing
- Guidelines
- The project's written instructions that define how to do a task and how to judge answers. On most projects they take precedence over personal preference.
- Instruction following
- Whether a model did exactly what was asked, including constraints such as length, format, language or things to avoid.
- Rubric
- A written list of criteria and scores used to judge a response, for example accuracy, instruction following and tone, each with clear pass or fail descriptions.
- AI interview
- A screening interview run by an AI system instead of a person, usually spoken or on video, with questions about your background and skills.
- Edge case
- An unusual item the guidelines do not clearly cover. Projects usually ask you to flag it rather than guess.
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