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# Data Labeling Specialist

- **Company:** Aerotek
- **Location:** San Jose, CA, United States
- **Salary:** $62,400.0
- **Contract:** Temporary to permanent
- **Skills:** Training Data, Computer Literacy, Machine Learning, Data Management, Video Streaming
- **Published:** October 15, 2026
- **Apply:** https://www.careercircle.com/jobs/all/all/usa/ca/san-jose/6af74a1a-a682-4131-b69d-d2e8aae77028

## About the Role

Data Entry
Data Labeling
Quick Learning
Detail Oriented
Data Management
Data Collection
Video Streaming
Health Advocacy
Machine Learning
Computer Literacy
Quality Assurance, * Strong attention to detail and the ability to apply consistent logic across diverse visual scenarios.

 * Comfort using computer-based tools and the ability to quickly learn new software and proprietary annotation platforms.
 * Proficiency with general computer skills and data entry in a production or lab environment.
 * Experience or familiarity with data labeling, data collection, and data management practices., Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.

## Description

The Data Labeling Specialist supports advanced AI and robotics initiatives by accurately annotating images and video streams generated by humanoid robots. This role focuses on consistent, high-quality data labeling, careful handling of edge cases, and continuous improvement of annotation workflows within a fast-paced, production-oriented environment., * Use proprietary annotation tools to label objects, poses, and interactions in images and video streams generated by humanoid robots.

 * Apply clear, consistent logic when annotating a wide range of visual scenarios to ensure high-quality training data for machine learning models.
 * Identify, document, and flag ambiguous or edge-case scenarios for review by the machine learning team.
 * Maintain high throughput while preserving accuracy and consistency across large batches of annotations.
 * Perform careful data entry and data management to ensure labeled datasets remain organized, traceable, and usable for downstream analysis.
 * Support quality assurance processes by reviewing labeled data, correcting errors, and helping to enforce annotation standards.