Data Quality Annotator @ Crossing Hurdles
placeHybrid
attach_money $83,200
businessHybrid
scheduleContractor
Posted 5 hours ago
Your Application Journey
Interview
Email Hiring Manager
***** @crossinghurdles.com
Recommended after applying
Job Details
About Data Quality Annotator
Crossing Hurdles is a recruitment firm connecting top candidates with leading AI research labs to build and train cutting-edge AI models.
Role Responsibilities
As a Data Quality Annotator, you will:
- Synthesize information from large volumes of data and organize it into structured formats.
- Annotate and categorize text, images, and other data according to detailed guidelines.
- Apply predefined rubrics and taxonomies to ensure high-quality, consistent outputs.
- Flag inconsistencies, ambiguities, or errors in datasets.
- Directly contribute to the improvement and accuracy of AI systems.
Requirements
Candidates should have the ability to synthesize complex or high-volume information into structured formats, excellent written communication skills, and preferably prior experience applying rubrics, taxonomies, or standardized guidelines.
Application Process
The process takes 20 minutes and involves:
- Uploading your resume.
- Completing an AI interview based on your resume (15 min).
- Submitting a form.
Key skills/competency
- Data Synthesis
- Annotation
- Categorization
- Rubrics
- Taxonomies
- Quality Assurance
- AI Systems
- Attention to Detail
- Data Organization
- Communication
How to Get Hired at Crossing Hurdles
🎯 Tips for Getting Hired
- Customize Resume: Tailor your resume to data annotation experiences.
- Prepare Examples: Highlight structured data and annotation work.
- Research Company: Review Crossing Hurdles values and success stories.
- Practice Interviews: Use AI interview tips and examples.
📝 Interview Preparation Advice
Technical Preparation
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Review data annotation guidelines.
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Practice data categorization exercises.
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Familiarize with annotation software.
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Study examples of structured data.
Behavioral Questions
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Describe handling tight deadlines.
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Explain teamwork under pressure.
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Discuss resolving data inconsistencies.
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Share communication strategies for remote work.
Frequently Asked Questions
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