Data Annotation Specialist - Computer Vision & Video Labeling

Wealth Recruitment•San Francisco, CA

About The Position

The Data Annotation Specialist supports computer vision and autonomous systems development by labeling and reviewing large-scale image and video datasets. The work contributes to improving machine learning models used in advanced perception systems. The role involves analytical, high-focus work in a fast-paced technical environment and may suit candidates with backgrounds in computer vision annotation, robotics, aviation, maritime operations, drone systems, military intelligence analysis, or sensor-based technologies. The position initially schedules 20–30 hours per week while the current project backlog is addressed. Hours may adjust based on project needs and performance. The role pays $20–$40 per hour.

Requirements

Must be a U.S. citizen for CUI eligibility.
Experience with computer vision labeling, robotics annotation, or video/image analysis.
Strong attention to detail and ability to maintain focus during repetitive visual review tasks.
Comfort working in proprietary internal software platforms.
Ability to consistently balance speed and accuracy in a production environment.
Strong pattern recognition and analytical skills.
Reliable internet connection and ability to work independently.

Nice To Haves

Background in maritime operations.
Background in drone/UAS operations.
Background in aviation or aircraft systems.
Background in military intelligence, ISR, or sensor analysis.
Background in robotics or autonomous systems.
Background in computer vision or video annotation environments.

Responsibilities

Perform high-volume image and video labeling using proprietary internal annotation tools.
Review visual datasets across multiple sensor and data types.
Identify, document, and flag model failure cases to support machine learning improvements.
Maintain labeling accuracy while meeting throughput and productivity targets.
Track progress across large datasets and ensure consistent performance.
Follow annotation guidelines and escalate unclear or edge-case scenarios when needed.
Work closely with technical teams to support rapid model development cycles.

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