QC Reviewer - Human Pose Keypoint Annotations

About the Role

We are looking for a detail-oriented QC Reviewer to ensure the accuracy and consistency of human pose keypoint annotations on thermal images. You will review annotator submissions within Label Studio , verify correct keypoint placement (including inferred positions for occluded body parts), and provide clear feedback to drive rework where needed.

Key Responsibilities

• Review completed keypoint annotations against the source thermal images, verifying person count and per-person annotation completeness.
• Check that each keypoint is placed as close as possible to the true anatomical center of the corresponding body part.
• Evaluate whether inferred keypoints (for occluded/obstructed body parts) are anatomically plausible and consistent with visible body positioning.
• Verify that confidence flags were applied appropriately for uncertain or difficult cases, and flag missing or misused confidence indicators.
• Identify systematic errors or patterns of confusion (e.g., consistent mislabeling in certain poses or occlusion types) and report them for annotator calibration.
• Provide specific, actionable feedback on rejected or revised annotations within Label Studio 's review workflow.
• Participate in periodic calibration sessions to align QC standards with the client's evolving guidelines or edge-case resolutions.
• Track and document review outcomes, common error types, and annotator performance trends as required.

Requirements

Required:

• Prior experience in computer vision annotation QA , review, or a related quality-control role.
• Strong understanding of human anatomy and pose structure to accurately assess keypoint placement, including inferred/occluded cases.
• Exceptional attention to detail — able to catch subtle placement errors, incorrect inferences, or misapplied confidence flags.
• Comfort working with thermal/infrared imagery and its visual limitations.
• Ability to apply detailed annotation/QC guidelines consistently and exercise sound judgment on ambiguous or edge-case scenarios.
• Strong written communication skills for providing clear, constructive feedback to annotators.
• Familiarity with Label Studio , including review/approval workflows, or willingness to quickly learn the platform.
• Reliable, self-directed work habits with the ability to meet review turnaround expectations.

Nice to Have:

• Direct experience with human pose estimation datasets (e.g., COCO keypoints ) or keypoint QA specifically.
• Experience with thermal/infrared imagery QA.
• Background in computer vision , robotics, or security/surveillance imaging.
• Experience managing structured QA/rework feedback loops for annotation teams.