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Data Annotation Manager Jobs in Washington (NOW HIRING)

Beyond SOF

DATA ENGINEER

Reston, VA · On-site

$119K - $143K/yr

Designs, implements, and operates data management systems for intelligence needs Designs how data ... Java -JDK 1.6+, Model View Controller (MVC) architecture, Annotation, Servelet 2.5/Java Server ...

Seekr

Senior Data Scientist

Reston, VA

Manage machine learning algorithm lifecycle * Support pre-sales efforts, identifying how the Seekr Platform could help satisfy customer requirements * Coordinate data collection and annotation ...

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Data Annotation Manager information

See Washington salary details

$35.1K

$110K

$194.8K

How much do data annotation manager jobs pay per year?

As of Aug 21, 2026, the average yearly pay for data annotation manager in Washington is $110,026.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,800.00 and $142,100.00 per year, depending on experience, location, and employer.

What does a data annotation manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

What are the key skills and qualifications needed to thrive as a data annotation manager?

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

What are some common challenges faced by data annotation managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

What is the difference between Data Annotation Manager vs Data Labeling Specialist?

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What are the most commonly searched types of Data Annotation jobs in Washington?

The most popular types of Data Annotation jobs in Washington are:

What job categories do people searching Data Annotation Manager jobs in Washington look for?

The top searched job categories for Data Annotation Manager jobs in Washington are:

What cities in Washington are hiring for Data Annotation Manager jobs?

Cities in Washington with the most Data Annotation Manager job openings:

Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in Washington as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $110,026 per year, or $52.9 per hour.

Data Scientist with Security Clearance

Fuse Engineering LLC

Fort George G Meade, MD • On-site

Other

Re-posted 8 days ago

Job description

Support for 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.  Requirements Clearance Required Top Secret SCI w/ Full Polygraph 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.  Must have some combination (2 or more) of the following skill areas:   Foundations: Mathematical, Computational, Statistical 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.  

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