Entry Level Ai Data Annotation information
See Elgin, IL salary details
$11.41 - $13.20
2% of jobs
$13.20 - $14.99
10% of jobs
$16.26 is the 25th percentile. Wages below this are outliers.
$14.99 - $16.78
18% of jobs
The median wage is $18.20 / hr.
$16.78 - $18.58
25% of jobs
$18.58 - $20.37
16% of jobs
$21.02 is the 75th percentile. Wages above this are outliers.
$20.37 - $22.16
11% of jobs
$22.16 - $23.96
8% of jobs
$23.96 - $25.75
4% of jobs
$25.75 - $27.54
2% of jobs
$27.54 - $29.34
2% of jobs
$29.34 - $31.13
1% of jobs
How much do entry level ai data annotation jobs pay per hour?
As of Aug 17, 2026, the average hourly pay for entry level ai data annotation in Elgin, IL is $20.01, according to ZipRecruiter salary data. Most workers in this role earn between $16.15 and $21.63 per hour, depending on experience, location, and employer.
An Entry Level AI Data Annotation job involves labeling and categorizing data such as images, text, audio, or video to help train artificial intelligence (AI) and machine learning models. Annotators follow specific guidelines to tag data accurately, ensuring that AI systems learn to recognize patterns correctly. These positions typically require attention to detail, basic computer skills, and the ability to follow instructions. No advanced technical knowledge is usually required, making it a great way to start a career in the AI or tech industry.
To thrive as an Entry Level AI Data Annotation Specialist, attention to detail, basic computer literacy, and a high school diploma or equivalent are typically required. Familiarity with data labeling platforms, annotation tools, and spreadsheet software is often expected. Strong organizational skills, focus, and the ability to work independently help individuals excel in this role. These skills ensure accurate and efficient data labeling, which is crucial for developing reliable AI and machine learning models.
Entry-level AI data annotation specialists often encounter challenges such as maintaining consistency and accuracy while labeling large volumes of data, understanding nuanced instructions, and adapting to changing project requirements. These challenges can be addressed by actively seeking clarification from team leads, participating in training sessions, and regularly reviewing annotation guidelines. Collaborating with teammates and using quality assurance feedback also helps improve accuracy and ensures alignment with project standards.
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