Jobs at Welo Global

Review, evaluate, and annotate AI-generated text, images, audio, and video; perform quality checks for accuracy and guideline compliance; identify edge cases and inconsistencies; contribute to dataset creation and calibration; provide feedback to improve annotation workflows and guidelines.

Review and proofread Bulgarian localized content for grammar, spelling, punctuation, consistency, formatting and cultural accuracy. Execute test cases, identify and report localization bugs in tracking systems, verify fixes, evaluate translations in software and apps, research linguistic issues, follow client style guides and complete tasks accurately and on time. Occasional translation from English to Bulgarian and basic troubleshooting/escalation as needed.

Review trainers' annotated image-question pairs to verify golden answers, flag errors or ambiguities, provide corrective feedback, track recurring issues, and escalate unclear cases to maintain dataset quality.

Review trainer-submitted image-question-answer pairs for accuracy and consistency against project guidelines; flag and correct incorrect or ambiguous answers; provide feedback, track error patterns, and escalate unclear cases to the project team.

Review trainer-submitted image/question/answer pairs to verify golden answers, flag errors or ambiguities, provide constructive feedback, track recurring issues, and escalate unclear cases to maintain dataset quality for an AI annotation project.

Review trainer-submitted image-question pairs and verify golden answers for accuracy and consistency. Flag and document incorrect, ambiguous, or incomplete responses, provide corrective feedback, track recurring errors, and escalate unclear cases. Ensure trainer output meets project quality standards.

Review trainer-submitted image question pairs and verify golden answers for accuracy and consistency. Flag and document incorrect, ambiguous, or incomplete responses, provide constructive feedback, track error patterns, and escalate unclear cases to the project team to ensure dataset quality.

Review trainer-submitted question-image-answer pairs to verify that golden answers match images and project guidelines. Flag incorrect, ambiguous, or inconsistent responses, provide corrective feedback, track recurring errors, and escalate unclear cases. Ensure trainer output meets quality standards before final sign-off.

Review annotated tasks from Trainers by comparing images, questions, and golden answers to confirm correctness and consistency. Flag and document incorrect, ambiguous, or incomplete responses, provide clear corrective feedback, track recurring error patterns, escalate unclear cases, and ensure outputs meet project quality standards before final sign-off.

Review trainer-submitted image-question pairs and verify 'golden' answers for accuracy and consistency, flagging errors, providing corrective feedback, tracking recurring issues, and escalating ambiguous cases to maintain data quality for an AI annotation project.

Review trainer-submitted image-question pairs and verify golden answers for accuracy and consistency. Flag errors or ambiguities, provide corrective feedback, track recurring issues, ensure outputs meet quality standards, and escalate unclear cases to the project team.

Review trainers' submitted QA tasks pairing images with pre-seeded questions and golden answers. Verify correctness, flag incorrect or ambiguous responses, provide constructive feedback, track recurring errors, ensure outputs meet quality standards, and escalate unclear cases to the project team.

Review trainer-submitted QA tasks pairing images with questions and golden answers, verify correctness against images, flag incorrect or ambiguous responses, provide constructive feedback, track recurring errors, ensure outputs meet quality standards, and escalate unclear cases to the project team.

Review trainer-submitted image-question pairs, verify golden answers against images, flag and document incorrect or ambiguous responses, provide corrective feedback, track recurring errors, and escalate unclear cases to maintain high dataset quality for an AI annotation project.

Review tasks submitted by Trainers: verify that the provided golden answer matches the image, flag incorrect or ambiguous responses, give constructive feedback, track recurring error patterns, ensure outputs meet quality standards, and escalate unclear cases to the project team.

Review annotated image-question pairs and verify golden answers for accuracy and consistency. Flag and correct errors, provide constructive feedback, track recurring issues, and escalate ambiguous cases. Ensure Trainer outputs meet project quality standards and report patterns to the project team.

Review trainers' annotated tasks (Korean), verify golden answers against images, flag errors or ambiguities, provide clear corrective feedback, track recurring issues, and escalate unclear cases to the project team to ensure dataset quality.

Review trainers' annotated tasks (questions, images, golden answers) to verify correctness and consistency against project guidelines. Flag and document incorrect, ambiguous, or inconsistent answers, provide constructive feedback, track recurring error patterns, and escalate unclear cases to the project team to ensure dataset quality before final sign-off.

Lead and build a high-performing sourcing function: develop sourcing strategies, build proactive talent pipelines, coach and mentor the sourcing team, provide market intelligence, partner with recruiters and hiring managers, engage passive candidates, and track sourcing performance to improve hiring outcomes.

Review trainers' completed annotation tasks by comparing questions, images, and golden answers for accuracy and consistency. Flag incorrect, ambiguous, or incomplete responses, provide constructive feedback, track recurring error patterns, and escalate unclear cases. Ensure output meets project quality standards before sign-off.