About the Role
The coordinator will be responsible for maintaining data integrity across AI training pipelines, identifying inconsistencies, and implementing corrective measures to enhance overall model accuracy.
Responsibilities
- Evaluate annotated datasets for accuracy and consistency
- Collaborate with data scientists to refine labeling guidelines
- Identify patterns in data discrepancies and recommend improvements
- Maintain detailed records of quality assurance checks
- Support the development of automated validation tools
- Review model outputs for alignment with expected results
- Escalate systemic data issues to senior team members
- Participate in regular calibration sessions with annotation teams
- Ensure compliance with project-specific quality benchmarks
- Track error rates and generate performance summaries
- Assist in training new team members on quality protocols
- Contribute to documentation for quality control processes
- Monitor incoming data batches for anomalies
- Provide feedback on labeling interface usability
- Coordinate with project managers on timeline adjustments
- Validate edge cases to improve model robustness
- Apply natural language understanding principles to assess text data
- Maintain neutrality and objectivity during data review
- Adapt quality criteria based on evolving project needs
- Work cross-functionally to align quality standards with business goals
- Use spreadsheets and internal tools to log quality metrics
- Respond to audit requests with accurate data samples
- Ensure adherence to data privacy standards
- Stay updated on AI quality best practices
- Propose process enhancements to reduce rework
Compensation
Competitive salary based on experience
Work Arrangement
Hybrid work model with partial remote flexibility
Team
Part of the AI training division focused on data quality and model refinement
About the Team
This role operates within a specialized unit dedicated to enhancing AI model performance through rigorous data evaluation. The team works on diverse language and technical projects, ensuring training data meets strict quality thresholds before being used in machine learning systems.
What We Value
We prioritize accuracy, consistency, and continuous improvement. Candidates who demonstrate a methodical approach to problem-solving and a commitment to high standards are a strong fit for this position.
Available for qualified candidates
