QC Lead - Physical AI Video Annotation

Arctic Engines is an enterprise-grade Al human data operations company specializing in high-quality training data, RLHF, and human feedback pipelines for frontier Al models. We are part of the Apna Group, one of India's fastest-growing unicorns, backed by marquee investors such as Lightspeed, Tiger Global, Insight Partners, Peak XV, and others. With native access to Apna's 60M+ workforce, we deliver high-quality training data at unmatched scale and speed.

We are looking for a QC Lead to own annotation quality for egocentric industrial video datasets. You will define review standards, lead the QC team, identify recurring errors, and ensure that delivered annotations meet project requirements.

• Direct experience with industrial video, robotics, or Physical AI datasets is mandatory.**
• Hands-on expertise in egocentric video annotation, temporal action segmentation, keypoint annotation, action taxonomies, and timestamped descriptions.
• Experience leading annotation QC teams and creating clear guidelines and calibration examples.
• Ability to analyze errors, run root-cause reviews, and turn findings into corrective action.
• Familiarity with video annotation tools and structured outputs such as JSON or CSV.

• →Lead reviewers and establish calibration, review, feedback, and rework processes.
• →Audit video chunking, temporal action boundaries, keypoint annotations, action labels, and natural language descriptions.
• →Check timestamp accuracy, coverage, label consistency, and the correctness of descriptions against the video.
• →Define QC checklists and sampling plans; track error rates, reviewer agreement, rejection trends, and quality improvements.
• →Resolve ambiguous cases, update guidelines, and coach annotators and reviewers.
• →Validate structured outputs and work with tooling teams to address workflow or export issues.