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Data Annotation Engineer Jobs in Washington, DC (NOW HIRING)

With experts in biomedical science, software engineering, and program management, we focus on ... annotation, DEG), and Digital Spatial Profiling (annotation, QC, normalization, spatial ...

With experts in biomedical science, software engineering, and program management, we focus on ... annotation, DEG), and Digital Spatial Profiling (annotation, QC, normalization, spatial ...

With experts in biomedical science, software engineering, and program management, we focus on ... annotation, DEG), and Digital Spatial Profiling (annotation, QC, normalization, spatial ...

Data Scientist 3

Annapolis, MD · On-site

$161K - $211K/yr

... annotation of language data with parts of speech information, and improve existing models by ... Ability to use analytic modeling, statistical analysis, programming, and/or another appropriate ...

Data Scientist 3

Annapolis, MD · On-site

$161K - $211K/yr

... annotation of language data with parts of speech information, and improve existing models by ... Ability to use analytic modeling, statistical analysis, programming, and/or another appropriate ...

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Data Annotation Engineer information

See Washington, DC salary details

$58.3K

$167K

$223.1K

How much do data annotation engineer jobs pay per year?

As of Jul 4, 2026, the average yearly pay for data annotation engineer in Washington, DC is $167,014.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,100.00 and $222,000.00 per year, depending on experience, location, and employer.

What are the main challenges faced by Data Annotation Engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What are the key skills and qualifications needed to thrive in the Data Annotation Engineer position, and why are they important?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

Does data annotation really pay?

Data annotation engineers can earn competitive wages, often paid hourly or per task, with pay rates varying based on experience, complexity of annotations, and the platform or employer. Entry-level roles may start at minimum wage, while experienced annotators or those with specialized skills can earn higher salaries or freelance rates. Overall, data annotation can provide a reliable income, especially for remote or flexible work arrangements.

What is the highest salary for data annotator?

The highest salary for a data annotation engineer can reach up to $80,000 to $100,000 annually, depending on experience, location, and the complexity of annotation tasks. Senior roles or those with specialized skills in tools like Labelbox or CVAT may earn higher compensation. Salaries vary widely across companies and regions but generally reflect the technical skills required for high-quality data labeling.

What is a data annotation engineer?

A data annotation engineer is a professional responsible for labeling and annotating data, such as images, text, or videos, to prepare it for machine learning models. They often use specialized tools and follow guidelines to ensure data quality, supporting the development of AI systems.

How hard is it to get hired by data annotation?

Getting hired as a data annotation engineer typically requires basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require advanced degrees, but strong accuracy and consistency are important for success in the role.

What is a Data Annotation Engineer job?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are popular job titles related to Data Annotation Engineer jobs in Washington, DC? For Data Annotation Engineer jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Data Annotation Engineer jobs in Washington, DC look for? The top searched job categories for Data Annotation Engineer jobs in Washington, DC are:
Infographic showing various Data Annotation Engineer job openings in Washington, DC as of June 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $167,014 per year, or $80.3 per hour.

Data Scientist 3 with Security Clearance

Gormat

Annapolis Junction, MD • On-site

Other

Posted 23 days ago


Job description

We are seeking a Data Scientist proficient in Python and Jupyter Notebook to support a Natural Language Processing (NLP) project. You will help to accurately and automatically tokenize language data with spoken or written origins, develop automated solutions for the annotation of language data with parts of speech information, and improve existing models by scoring performance against human-generated annotations for speech and text. The Level 3 Data Scientist shall possess the following capabilities: * Foundations: (Mathematical, Computational, Statistical).

* Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility). * Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations). * Ability to make and communicate principal conclusions from data using elements of mathematics, statistics, computer science, and applications-specific knowledge.

* Ability to use analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique feature and limitations inherent in Government data holdings. * Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. * Effectively communicate complex technical information to non-technical audiences.

  * DS position in X32 as a support for a Natural Language Processing (NLP) project to accurately and automatically tokenize language data with spoken or written origins; develop automated solutions for the annotation of language data with parts of speech information, and improved existing models by scoring performance against human-generated annotations for speech and text. Qualifications: * Bachelor's Degree with 10 years of relevant experience, associate's degree with 12 years of experience may be considered for individuals with in-depth experience that is clearly related to the position. * Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g.

physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, , data structures, data mining, artificial intelligence).

College-level requirement, or upper-level math courses designated as elementary or basic do not count. * Broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university. * Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least on high level language (e.g.

Python), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering.

TS/SCI with polygraph is required.