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Data Annotator Jobs in Reston, VA (NOW HIRING)

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Data Annotator information

See Reston, VA salary details

$47.9K

$171.7K

$253.3K

How much do data annotator jobs pay per year?

As of Jun 13, 2026, the average yearly pay for data annotator in Reston, VA is $171,677.00, according to ZipRecruiter salary data. Most workers in this role earn between $138,900.00 and $176,900.00 per year, depending on experience, location, and employer.

What are Data Annotators?

Data Annotators are professionals who label or tag data such as images, text, audio, or video to prepare it for use in machine learning and artificial intelligence (AI) models. Their work is crucial because accurately labeled data helps algorithms learn to recognize patterns and make decisions. Data annotators may use specialized software tools to highlight objects, transcribe speech, or classify documents according to specific guidelines. The quality and accuracy of their annotations directly affect the performance of AI systems.

What is the difference between Data Annotator vs Data Labeler?

AspectData AnnotatorData Labeler
Required CredentialsHigh school diploma or equivalent; some roles may prefer basic technical skillsSimilar; often requires only basic education and attention to detail
Work EnvironmentRemote or office-based; working with datasets and annotation toolsPrimarily remote; focused on labeling data for machine learning
Industry UsageUsed across AI, machine learning, and data science industriesCommonly used in AI and machine learning sectors for training data
Search & Comparison IntentOften compared due to similar tasks and roles in data preparation

Both Data Annotators and Data Labelers perform data preparation tasks for AI models, often with overlapping skills and work environments. The main difference lies in terminology used by employers or platforms, but their roles are largely similar, focusing on labeling data to improve machine learning algorithms.

How much do data annotators get paid?

Data annotators typically earn between $10 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Some positions may offer fixed project-based pay or part-time schedules, especially in remote or freelance settings.

What are the typical challenges Data Annotators face when working with large datasets, and how can they overcome them?

Data Annotators often encounter challenges such as repetitive tasks, maintaining high accuracy, and dealing with ambiguous data points when working with large datasets. To overcome these, it's important to follow clear annotation guidelines, regularly communicate with team leads or project managers about uncertainties, and leverage quality control tools provided by the organization. Collaborating with peers and participating in review sessions can also help ensure consistency and improve the overall quality of the annotations.

What qualifications do you need to be a data annotator?

Data annotators typically need a high school diploma or equivalent, strong attention to detail, and basic computer skills. Familiarity with data labeling tools and the ability to follow instructions accurately are also important for success in this role.

What does a data annotator do?

A data annotator labels and tags data such as images, text, or videos to help machine learning models understand and learn from the data. They use tools and follow guidelines to ensure accuracy and consistency, often working with large datasets in a structured environment. This role requires attention to detail and knowledge of annotation tools or software.

What are the key skills and qualifications needed to thrive as a Data Annotator, and why are they important?

To thrive as a Data Annotator, you need attention to detail, accuracy, and a basic understanding of data structures, often supported by a high school diploma or equivalent. Familiarity with data labeling platforms, annotation tools like Labelbox or Supervisely, and sometimes basic coding skills are typically required. Strong organizational skills, patience, and the ability to follow precise guidelines make someone stand out in this position. These skills and qualities are crucial for producing high-quality datasets that drive effective machine learning and AI model development.

Is data annotation well paid?

Data annotation jobs typically offer hourly wages that are close to minimum wage or slightly above, depending on the employer and location. While some companies pay competitive rates, many entry-level positions are low-paying, and pay can increase with experience, skills, or specialized tools knowledge. Overall, data annotation is generally not considered a high-paying role but can provide steady work for those interested in AI and machine learning fields.
What job categories do people searching Data Annotator jobs in Reston, VA look for? The top searched job categories for Data Annotator jobs in Reston, VA are:
What cities near Reston, VA are hiring for Data Annotator jobs? Cities near Reston, VA with the most Data Annotator job openings:
Data Annotator jobs near you
Assistant Research Scientist (PREP0004176)

Assistant Research Scientist (PREP0004176)

Johns Hopkins University

Gaithersburg, MD • On-site

Full-time

Posted 6 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 200 frontline employees who took The Breakroom Quiz

224th of 872 rated healthcare providers

Job description

Description
PREP Research Associate
This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title:
Reliability of Human and LLM Annotations for AI Risk Assessment
The work will entail:
This project focuses on using Large Language Models (LLMs) to provide annotations of evaluation data (a.k.a., LLM as judge), and the design of an Inter-Annotator Agreement study to assess the reliability of both human and LLM annotations. The candidate will explore assessing the indicators of a given AI-related risk, determining how to identify them, and providing annotators with examples to annotate the presence of various risks. The project aims to develop an annotation framework for AI risk assessment and establish metrics for data quality in AI risk research, supporting broader work at NIST in assessing and measuring the validity and reliability of AI-related risks in data annotation.
U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
  • Gain familiarity with existing literature on data annotation and LLM as judge
  • Understand NIST's role and ongoing efforts in assessing and measuring the validity and reliability of AI-related risks in data annotation
  • Contribute to developing an annotation framework for AI risk assessment
  • Collaborate effectively with cross-functional and interdisciplinary stakeholders to ensure successful project outcomes

Deliverables
  • Contributions to a NIST report that supports ongoing NIST AI evaluation efforts focused on the design of an Inter-Annotator Agreement to assess the reliability of both human and LLM annotations.

Qualifications
  • Background in Computer Science, Data Science, or related field.
  • Education level: Bachelor's or Graduate Degree
  • Strong interest in data annotation and AI risks
  • Familiarity with scientific reading and technical writing

Application Instructions
Please upload the following with your application:
• CV/Resume
*Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.
• Self portraits
• Phone number
• Home address/Country
• Citizenship status
• Languages spoken
• Sex/Gender
Privacy Act Statement
Authority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.

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