Overview

AI Data Annotators play a critical role in training next-generation humanoid and mobile robots. They transform raw video footage into structured, high-quality datasets by labeling scenes, actions, objects, and risks according to Frontier AI’s annotation standards and Ontology v1.0.

This role ensures that datasets used by OEMs and research labs are accurate, consistent, and reliable — forming the foundation for robotics perception, manipulation, navigation, and safety models.

Key Responsibilities

1. Video Annotation

• Label human actions, object interactions, environments, and scene context in short video clips.
• Identify keyframes, track movement, and apply bounding boxes, segmentation masks, or action tags (depending on task).
• Annotate risk events (falls, collisions, proximity violations, unsafe handling, etc.) with precision.

2. Ontology Application

• Follow Frontier AI’s Ontology v1.0 for:
  • Scene types (home, warehouse, office, factory, etc.)
  • Human behaviours (grasp, place, push, lift, rotate, inspect…)
  • Objects & categories (tools, utensils, containers, appliances, fixtures)
  • Risk factors (slip hazards, poor visibility, blocked pathways, incorrect posture)

3. Quality Assurance

• Meet or exceed internal accuracy benchmarks.
• Review peers’ annotations and provide constructive feedback.
• Correct inconsistencies or ambiguous labels.
• Flag unclear footage or potential ontology gaps to the lead annotator.

4. Tooling & Workflow

• Use Frontier AI’s preferred annotation tools (well known tools + custom in-house tools).
• Follow workflow guidelines: clip review → annotation → QA → submit.
• Maintain target throughput and meet project deadlines.

Success Metrics

• Annotation accuracy (consistency with Ontology)
• Speed / throughput
• Quality of peer reviews
• Attention to detail
• Reliability & communication with team leads

Required Skills

• Strong attention to detail and pattern recognition
• Ability to follow structured guidelines precisely
• Comfortable working with video footage and annotation tools
• Good written communication for notes and describing edge-cases
• Basic understanding of human motion, objects, and environmental cues

Experience in AI, robotics, or machine learning is a bonus, but not required — training provided.

Who This Role Suits

• People who enjoy structured, detail-oriented work
• Individuals interested in robotics, AI, or future technologies
• Those who want to contribute to training next-generation humanoid robots

Optional Add-Ons

Compensation

• Monthly salary or flexible contracts
• Performance bonuses
• Remote flexibility some of the time

Career Paths

• Senior Annotator
• Annotation QA Specialist
• Ontology Designer
• Data Operations Lead

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