3 days ago

Data Annotation Specialist

HireArt

Hybrid
Full Time
$72,800
Hybrid

Job Overview

Job TitleData Annotation Specialist
Job TypeFull Time
CategoryCommerce
Experience5 Years
DegreeMaster
Offered Salary$72,800
LocationHybrid

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Job Description

Job Summary

HireArt is assisting a client in finding a Data Annotation Specialist to join their Data Operations team. In this role, you will significantly contribute to annotation quality assurance efforts, collaborating closely with machine learning engineers and cross-functional stakeholders. Your responsibilities will include defining quality metrics, ensuring high-quality labeling output, and continuously improving data annotation workflows and operational processes.

The ideal candidate is a quick learner, adapts easily to new tools and concepts, and brings strong attention to detail to repetitive, quality-focused work.

Responsibilities

  • Work closely with external vendors and machine learning engineers to curate, review, and deliver high-quality labeled datasets.
  • Use third-party and proprietary tools to perform quality assurance on annotated tasks.
  • Support, develop, and improve data labeling workflows and operational processes.
  • Provide actionable feedback to improve annotation tools and increase throughput.
  • Support the Data Annotation Operations Lead with ongoing QA and process initiatives.

Requirements

  • Bachelor’s degree or equivalent practical experience.
  • Experience with data annotation, data analysis, or relevant domain experience (e.g., driving-related datasets).
  • Comfort working with visual and spatial software in 2D and 3D environments on repetitive tasks (e.g., QGIS, ArcGIS, or similar tools).
  • Ability to learn quickly and implement new concepts and workflows.
  • Strong attention to detail and commitment to quality.
  • Clear communication and strong interpersonal skills.

Benefits (via HireArt)

  • PTO and paid holidays.
  • Pre-tax commuter benefits.
  • Employer (HireArt) subsidized Healthcare Benefits.
  • Flexible Spending Account for healthcare-related costs.
  • HireArt covers all costs for short- and long-term disability and life insurance.
  • 401k package.

Commitment

This is a 6-month contract position staffed via HireArt. The role is remote and open to U.S.-based candidates who are able to work PST hours. Preference will be given to candidates located in the Bay Area.

HireArt values diversity and is an Equal Opportunity Employer. We are interested in every qualified candidate who is eligible to work in the United States. Unfortunately, we are not able to sponsor visas or employ corp-to-corp.

Key skills/competency

  • Data Annotation
  • Quality Assurance
  • Machine Learning
  • Data Labeling
  • Workflow Optimization
  • Process Improvement
  • Data Curation
  • Stakeholder Collaboration
  • QGIS/ArcGIS
  • Attention to Detail

Tags:

Data Annotation Specialist
Data Labeling Specialist
QA Specialist
data annotation
quality assurance
machine learning
data labeling
workflow improvement
process optimization
data curation
stakeholder collaboration
feedback
operational support
QGIS
ArcGIS
visual software
spatial software
proprietary tools
2D environments
3D environments
machine learning tools
data analysis tools

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How to Get Hired at HireArt

  • Research HireArt's client: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor to understand their culture.
  • Customize your resume: Highlight specific experience in data annotation, quality assurance, and using visual/spatial software relevant to the Data Annotation Specialist role.
  • Show attention to detail: Emphasize your commitment to accuracy and quality throughout your application materials and during interviews for the Data Annotation Specialist position.
  • Prepare for skill assessments: Practice using visual and spatial software, such as QGIS or ArcGIS, and be ready to discuss your experience with repetitive, quality-focused tasks.
  • Demonstrate collaboration skills: Be prepared to discuss past experiences working effectively with machine learning engineers and cross-functional stakeholders in a data operations environment.

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