Data Annotation
QC Lead - Physical AI Video Annotation
About the role
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.
From Arctic Engines's job posting, checked Oct 30, 2026. The employer's own page is the final word on requirements and availability.
The application
Your details and CV, then 11 questions (1 written).
- 1.HeadlineShort answer · optional
- 2.EducationShort answer · optional
- 3.ExperienceShort answer · optional
- 4.SummaryWritten answer · optional
- 5.Do you have direct experience working on industrial video, robotics, or Physical AI datasets?Pick any: 6521682 · 6521683 · 6521684
- 6.How many years of experience do you have in video annotation quality control / QA?Pick any: 6521685 · 6521686 · 6521687 · 6521688
- 7.Have you led a team of annotation reviewers / QC analysts?Pick any: 6521689 · 6521690 · 6521691
- 8.Which of the following video annotation tasks have you handled hands-on?Pick any: 6521692 · 6521693 · 6521694 · 6521695
- 9.Which metrics have you used to track annotation quality?Pick any: 6521696 · 6521697 · 6521698 · 6521699
- 10.Are you available to work full-time from the Bengaluru office?Yes or no
- 11.What is your current availability to join?Pick any: 6521700 · 6521701 · 6521702 · 6521703
From Arctic Engines's apply page, checked Oct 30, 2026.
About Arctic Engines
The human-data arm of India's Apna Group hires onsite video collectors, freelance video annotators and annotation leads for its physical-AI egocentric video programs.
How it pays: The Operations Lead and QC Lead are full-time posts; the Video Annotator is freelance, paid per video hour. The postings state no payment schedule.
From our research: Embodied-AI Data Annotation market (market report)
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