QC Lead - Physical AI Video Annotation

Apna; Bengaluru; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Arctic Engines in Bengaluru seeks 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 delivered annotations meet project requirements.

Responsibilities include calibrations, auditing timestamps and labels, and continuous quality improvement through clear guidelines and coaching of annotators.

Qualifications

• 3+ years in video annotation QC with leadership experience.
• Direct experience with industrial video datasets.
• Expert in egocentric video annotation and temporal action segmentation.
• Familiar with JSON/CSV structured outputs.

Responsibilities

• Lead reviewers and establish calibration, review, feedback, and rework processes.
• Audit video chunking, temporal action boundaries, keypoint annotations, action labels, and descriptions.
• Check timestamp accuracy, coverage, label consistency, and description correctness against video.
• Define QC checklists and sampling plans; track error rates and reviewer agreement.
• Resolve ambiguous cases, update guidelines, and coach annotators and reviewers.
• Validate structured outputs and coordinate with tooling teams to fix workflow or export issues.

Skills

QC leadership

Video annotation

Root-cause analysis

Guidelines creation

Tooling coordination

Tools

JSON

CSV

Job 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.

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.