Video Annotator - Physical AI

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.

Role: Video Annotator - Physical AI

Requirement: 50+

Company: Arctic Engines

Location: Remote | Freelancer

Experience: 1+ year in Physical AI or robotics video annotation

Joining: Immediate joiners preferred

Compensation : Rs 750/video hour

About The Role We are hiring Video Annotators to create accurate, timestamped annotations for egocentric videos captured in industrial and commercial environments. Candidates must have hands-on experience working on Physical AI datasets .

Requirements Key responsibilities

• Review and annotate first-person video showing human actions, tools, objects, and task workflows
• Split videos into meaningful chunks and mark precise start and end times for each action
• Apply action labels and write clear natural language descriptions of what happens in each segment
• Review model-generated annotations, correct errors, and ensure complete coverage and consistent labeling
• Follow annotation guidelines, resolve unclear cases with reviewers, and complete rework within assigned timelines

Required skills:

• Prior hands-on experience annotating Physical AI, robotics, or industrial egocentric video is mandatory
• Understanding of temporal action segmentation, video chunking, action labeling, and timestamp accuracy
• Ability to describe visible actions clearly, including the relevant hand, object, and tool
• Experience with video annotation platforms
• Strong attention to detail and ability to meet quality and productivity targets

Note: Apply through this platform with your CV and a brief description of the Physical AI annotation projects you have worked on.