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Freelance Machine Learning Data Annotation Jobs in Silver Spring, MD

Seekr

Senior Data Scientist

Reston, VA · On-site

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

Seekr

Senior Data Scientist

Reston, VA · On-site

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

General Motors

Senior AI/ML Engineer

Washington, DC · On-site +1

$118K - $162K/yr

... data annotation (pre-labeling, autolabeling, active learning loops), helping us move from human-only to machine-led labeling at scale. * Champion AI-assisted engineering Use and advocate for modern ...

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Freelance Machine Learning Data Annotation information

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$13

$22

$36

How much do freelance machine learning data annotation jobs pay per hour?

As of Jun 18, 2026, the average hourly pay for freelance machine learning data annotation in Silver Spring, MD is $22.61, according to ZipRecruiter salary data. Most workers in this role earn between $17.88 and $25.87 per hour, depending on experience, location, and employer.

What is the difference between Freelance Machine Learning Data Annotation vs Data Labeler?

AspectFreelance Machine Learning Data AnnotationData Labeler
CredentialsBasic understanding of annotation tools, sometimes with specialized domain knowledgeTypically no formal credentials required
Work EnvironmentRemote, flexible, project-basedOften remote or in-house, depending on employer
Industry UsageUsed in AI/ML development for training datasetsUsed in data preparation for various industries, including AI
Search/Comparison IntentFocuses on freelance opportunities, project scope, and toolsMore general, often employed by companies for data labeling tasks

Freelance Machine Learning Data Annotation involves independently completing annotation tasks for AI models, often with specialized tools and domain knowledge. Data Labelers typically perform similar tasks but may work as employees or contractors within a company. The main difference lies in the freelance nature and project-based work of data annotation roles.

What are the key skills and qualifications needed to thrive as a Freelance Machine Learning Data Annotation specialist, and why are they important?

To thrive as a Freelance Machine Learning Data Annotation specialist, you need attention to detail, basic knowledge of data labeling concepts, and familiarity with machine learning data types. Experience with annotation tools (such as Labelbox, RectLabel, or CVAT) and understanding of data privacy protocols are commonly required. Strong communication, time management, and the ability to follow complex guidelines are essential soft skills for delivering accurate results. These skills ensure high-quality, consistent data annotation, which is critical for effective machine learning model training and performance.

What is freelance machine learning data annotation?

Freelance machine learning data annotation involves labeling or tagging data—such as images, text, audio, or video—to help train machine learning models. As a freelancer, you work independently or through platforms, completing specific annotation tasks assigned by companies or researchers. This work is essential because high-quality labeled data is required for AI systems to learn and make accurate predictions. Annotators may categorize images, transcribe speech, or highlight relevant information in documents. The flexibility of freelancing allows you to choose projects and work remotely.

What are some common challenges faced by freelance machine learning data annotators, and how can they be managed?

Freelance machine learning data annotators often encounter challenges such as maintaining data accuracy, handling repetitive tasks, and understanding complex annotation guidelines. Staying organized and regularly reviewing project instructions can help ensure consistency and quality in annotations. Additionally, communicating proactively with project managers and utilizing annotation tools efficiently can help manage workload and clarify uncertainties. Building expertise in different data types (text, image, audio) also allows annotators to diversify their projects and reduce monotony.
What are popular job titles related to Freelance Machine Learning Data Annotation jobs in Silver Spring, MD? For Freelance Machine Learning Data Annotation jobs in Silver Spring, MD, the most frequently searched job titles are:
What job categories do people searching Freelance Machine Learning Data Annotation jobs in Silver Spring, MD look for? The top searched job categories for Freelance Machine Learning Data Annotation jobs in Silver Spring, MD are:
What cities near Silver Spring, MD are hiring for Freelance Machine Learning Data Annotation jobs? Cities near Silver Spring, MD with the most Freelance Machine Learning Data Annotation job openings:
Freelance Machine Learning Data Annotation jobs near you

Data Scientist III with Security Clearance

Black Eagle Defense

Fort George G Meade, MD • On-site

$128K - $185K/yr

Other

Posted 12 days ago

Job description

Job Description SALARY RANGE $128,000 - $185,000/year DUTIES As a successful candidate for the Data Scientist III role, you will devise strategies for extracting meaning and value from large datasets. Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge. Through 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 features and limitations inherent in NSA/CSS data holdings.

Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others in withdrawing appropriate conclusions from the analysis of such data. Effectively communicate complex technical information to non-technical audiences. Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting NSA/CSS collection, processing, storage, and analytic capabilities and limitations.

Required Skills SKILLS Employ some combination (2 or more) of the following skill areas: I. Foundations: (Mathematical, Computational, Statistical) II. Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility) III.

Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations) QUALIFICATIONS A Bachelor's Degree with 10 years of relevant experience or an 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 requirements, or upper-level math courses designated as elementary or basic do not count.

A 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 one 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. Experience in more than three areas is strongly preferred.

Additional Requirements: • 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 • Demonstrated NLP experience

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