Website:
imerit.ai
Job details:
Job Role - Quality Lead - AV Domain
Remote Opportunity.
ROLE SUMMARY
The Quality Lead owns the quality outcomes for an annotation pipeline or account segment. This role sits between the Trainer and the Team Leads: it turns training standards into day-to-day quality enforcement, and turns floor-level error patterns into feedback that improves training and calibration. The Quality Lead does not manage headcount directly — it drives the quality pillar of the pipeline through audits, calibration, root-cause analysis, and coaching support delivered in partnership with Team Leads.
KEY RESPONSIBILITIES
- Own the quality audit cadence for the pipeline: sampling plans, QA reviews, and accuracy/precision scoring against client rubrics.
- Run root-cause analysis on quality misses and defects, distinguishing training gaps, tooling issues, guideline ambiguity, and individual performance issues.
- Partner with the Trainer to translate recurring error patterns into updated training materials, calibration sessions, and onboarding content, and join the Trainer in bringing findings to the client for discussion.
- Track training rollout and resulting adjustments against the quality goal, and tighten the feedback loop between findings, training, and measured progress.
- Partner with Team Leads to deliver targeted coaching to Team Members, using QA data to prioritize who and what gets coached.
- Maintain and evolve quality guidelines/rubrics in response to client feedback, guideline changes, or edge cases surfaced from the floor.
- Run calibration sessions across Team Leads and reviewers to keep scoring consistent pipeline-wide.
- Report quality metrics and trends to DataOps leadership and the Project Manager, flagging systemic risk before it affects client-facing quality SLAs.
- Represent quality in client-facing reporting and audits alongside the Project Manager when needed.
HOW THIS ROLE WORKS WITH OTHERS
- Trainer — upstream partner, driven by Quality Lead findings. The Quality Lead's audit data and error trends are the input; the Trainer crafts the training resources (updated materials, calibration sessions, refreshers) and drives getting that content into the team. The Quality Lead and Trainer jointly bring findings to the client for discussion. After rollout, the Quality Lead tracks the training and any adjustments against the quality goal to confirm progress is actually happening, and keeps that feedback loop tight rather than waiting for the next audit cycle.
- Team Lead — closest working partner. Team Lead owns day-to-day people management, attendance, and productivity of Team Members; Quality Lead supplies the quality data and coaching priorities the Team Lead acts on. Neither manages the other.
- Group Lead — primary peer partner, not a manager. The Quality Lead and Group Lead work alongside each other on pod quality: the Quality Lead brings audit data, defect trends, and coaching priorities; the Group Lead sets pod-level priorities, allocates Team Lead attention, and clears roadblocks. Quality decisions that affect staffing, targets, or client commitments are made together, but the Quality Lead reports into DataOps, not to the Group Lead.
- Project Manager — account owner. Consumes quality trends and risk flags for client reporting and account strategy; the Quality Lead escalates systemic or client-visible quality risk directly when urgent.
REQUIREMENTS
- 1+ years as a Team Lead, senior annotator, or QA reviewer within an annotation/DataOps pipeline.
- Strong grasp of the pipeline's guidelines and edge cases; able to make and defend judgment calls on ambiguous data.
- Comfortable working in spreadsheets/dashboards to analyze error trends (Excel/Sheets required; SQL or BI tool experience a plus).
- Clear written and verbal communication for coaching feedback and calibration sessions.
- Experience giving feedback to peers or Team Members without formal management authority.
SUCCESS METRICS
- Pipeline accuracy/quality score vs. client SLA.
- Defect recurrence rate (same root cause reappearing after a fix cycle).
- Calibration consistency across Team Leads/reviewers (inter-rater agreement).
- Time from defect detection to guideline/training correction.
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