QC Lead - Physical AI Video Annotation in Bangalore, भारत
Apna
Full-timeStandard weekly hours
Bangalore, भारत
About Arctic Engine:
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.
Company: Arctic Engines
Requirement: 1
Location: Bengaluru (Work from office - Domlur | 6 days)
Employment: Full-time
Experience: 3+ years in video annotation quality assurance, including team leadership
Joining: Immediate joiners preferred
Requirement: 1
CTC:
About the role
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.
Requirements
Responsibilities
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.
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.
Apply through this platform with your CV and a brief summary of the video annotation QC programs you have led.
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