Hungarian Team Lead

SME Careers; United States; Remote; USD 41,000 - 83,000; Part time

Job summary

SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, seeks a Hungarian Quality Assurance Lead to oversee quality across remote Hungarian AI training projects. You will review AI-generated Hungarian content, provide precise feedback, and ensure adherence to project rubrics.

Strong Hungarian and English skills, attention to detail, and remote collaboration are essential. Selection involves an AI interview, a domain task, and a recruiter interview.

Qualifications

• Native or near-native Hungarian proficiency with strong reading and writing skills.
• Strong English reading and writing to follow project guidelines and provide feedback in English.
• 3+ years of professional experience in Hungarian language workflows (writing, editing, translation, QA).
• Experience leading or supporting remote teams of trainers, annotators, reviewers, or QAs.
• Familiarity with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management tools.

Responsibilities

• Quality monitoring: spot-check Hungarian items and provide ongoing feedback.
• Communicate updates to trainers and QAs about guidelines and workflow changes.
• Answer questions about Hungarian wording, grammar, accents, tone, and terminology.
• Activate inactive contributors and track follow-ups to maintain engagement.
• Create and maintain Hungarian project documentation, guides, FAQs, and onboarding materials.
• Lead onboarding/training calls to explain expectations and rubrics.
• Ensure consistent application of Hungarian language guidelines across projects.
• Identify recurring quality gaps and propose scalable QA processes.

Skills

Hungarian writing

Hungarian speaking

English communication

Remote team leadership

Detail orientation

Education

Bachelor’s or Master’s degree in a relevant field

Job description

In this hourly, remote contractor role, you will work as a Hungarian Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Hungarian AI training projects. You will review AI-generated Hungarian 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 Hungarian 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 Hungarian quality leadership will directly help improve the world’s premier AI models by ensuring that Hungarian 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 Hungarian items, identify quality issues, provide ongoing feedback through DMs, and elevate 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 Hungarian wording, grammar, accents/diacritics, 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 Hungarian 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 Hungarian-specific style requirements.
• Quality alignment: Ensure all trainers and QAs apply Hungarian 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 Hungarian-language projects.

Requirements

• Bachelor’s or Master’s degree in Hungarian, Linguistics, Translation, Communications, Journalism, English, Education, Quality Assurance, or a relevant domain/related field.
• Native or near-native Hungarian 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 Hungarian writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review workflows.
• Strong understanding of Hungarian grammar, spelling conventions, punctuation, vowel harmony, suffixation, word order, tone, register, and cultural context.
• Ability to evaluate Hungarian content against detailed rubrics and identify issues such as mistranslation, literal phrasing, unnatural tone, incorrect suffix usage, missing accents/diacritics, hallucinated claims, ambiguity, or inconsistent terminology.
• 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.