Quality Control Lead – Physical AI Video Annotation

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.

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.