# QC Lead - Physical AI Video Annotation at apna

> India · Staff · Other · open. Source: https://alderhq.app/jobs/workable%3Aapna%3A9281C1D6F4 (read 2026-11-08).

## Facts (observed)

- Company: [apna](https://alderhq.app/companies/apna)
- Location text: Bengaluru, Karnataka, India
- Where it can be done from (inferred): India (evidence: "Location lists "bengaluru"")
- Posted: 2026-10-03 (36 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://apply.workable.com/j/9281C1D6F4

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Bengaluru, n 7): p25 ₹31.3L, median ₹64.8L, p75 ₹114L

## Description (the employer's text)

### 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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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/workable%3Aapna%3A9281C1D6F4 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.