Job Summary:
Welo Data is seeking detail-oriented individuals to join their team as Data Labeling Associates. The role involves evaluating AI model outputs, providing structured feedback, and contributing to the improvement of evaluation frameworks while ensuring data quality and model performance.
Responsibilities:
โข Evaluate AI model outputs and provide structured, high-quality feedback
โข Perform audit-based reviews of data and model behavior โ identifying patterns, edge cases, and failure modes
โข Apply guidelines thoughtfully โ and flag when they donโt reflect real-world scenarios
โข Contribute to improving evaluation frameworks, not just executing them
โข Identify trends in model performance and communicate insights clearly
โข Participate in team discussions, calibrations, and stakeholder syncs
โข Partner with leads and cross-functional teams to refine quality standards
โข Document findings in a clear, concise, and actionable way
Qualifications:
Required:
โข Native-level language proficiency
โข University degree (Bachelorโs or higher)
โข B2 or superior level of English
โข 1โ2 years of professional writing experience with strong, structured writing skills
โข Ability to apply complex writing rules and guidelines consistently
โข Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience
โข Strong critical thinking and attention to detail
โข Ability to make sound judgment calls in ambiguous situations
โข Naturally curious about AI, technology, and how systems behave
โข Comfortable speaking up, asking questions, and contributing ideas
โข Strong written and verbal communication skills
โข Ability to stay consistent while working with evolving guidelines
Preferred:
โข Experience in data quality, QA, annotation, or analysis is helpful โ but not required
Company:
With 27+ years of experience, Welo Data is the human-centered infrastructure for globally effective AI. Founded in , the company is headquartered in , , with a team of 1001-5000 employees. The company is currently Late Stage.