# QC Lead - Physical AI Video Annotation

**Company:** [Apna](https://jobs.workable.com/companies/oGcPwdAqKGbezdH7GyyVKX.md)
**Location:** Bengaluru, India
**Workplace:** on site
**Department:** Operations

[Apply for this job](https://jobs.workable.com/view/79683bc4-3688-40b2-9ef2-5e0781fd67bf)

## Description

**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.**