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# Data Annotation Specialist

- **Company:** Continuum Resource Network
- **Location:** Sunnyvale, CA, United States
- **Salary:** $41,600.0 - $72,800.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Computer Vision, Big Data, Proprietary Software
- **Published:** May 16, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2e1f1132279824f2

## About the Role

Do you have experience in Data entry?, * US Citizenship is a MUST (Required for CUI eligibility).

 * Experience with CV (Computer Vision) labeling, robotics annotation, or video analysis.
 * High cognitive endurance for complex visual tasks.
 * Ability to work directly in internal software (no third-party labeling services).
 * Ideally someone with a maritime, drone or aircraft experience (probably military background), * CV labeling, robotics annotation, or video analysis: 1 year (Preferred)
 * prolonged concentration, visual accuracy: 1 year (Preferred)
 * High cognitive endurance for detailed visual tasks: 1 year (Preferred)

## Description

We are helping our client find a Data Annotation Specialists to help clear a high-volume data backlog and support the company's computer vision pipeline.

In this role, you'll focus on labeling large volumes of image and video data using internal annotation tools. By shifting labeling work from engineering teams to dedicated specialists, this role helps accelerate model development cycles and improve the quality of training data used in computer vision systems.

The ideal candidate is detail-oriented, comfortable working with visual data for extended periods, and able to maintain both speed and accuracy in a high-volume labeling environment.

Schedule:
20-30 hours per week until the current backlog is cleared. Hours may be adjusted afterward based on project needs.

As a Data Annotation Specialist, you'll:

 * Perform high-volume image and video labeling using internal proprietary annotation tools.
 * Identify and flag model failure cases across multiple sensor and data types.
 * Maintain strong labeling accuracy while meeting throughput targets.
 * Track progress and ensure consistent performance across large datasets.
 * Follow established labeling guidelines and escalate ambiguous cases when needed.


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