QC Lead - Physical AI Video Annotation
- Location
- Bengaluru, Karnataka, India
- Work mode
- On-site
- Employment type
- Full-time
About the job
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.
- 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.
- 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.
From Apna's careers page (Workable), checked 28 Oct 2026.
How to apply
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- Fill in the form and upload your résumé there. Jobiwork never sees your application.
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Posted 3 Oct 2026 · checked 28 Oct 2026 · Report this job