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Data Labeling Jobs in Virginia (NOW HIRING)

Novul Solutions

Data Analyst

Arlington, VA · On-site

Manage the coordination and deployment of data tagging and labeling mechanisms across the DoW SAP enterprise. * Ensure compliance with DoW policies on data classification and information security ...

Novul Solutions

Data Analyst

Arlington, VA

$100K - $135K/yr

Manage the coordination and deployment of data tagging and labeling mechanisms across the DoW SAP enterprise. * Ensure compliance with DoW policies on data classification and information security ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA · On-site

We are looking for seasoned Data Scientist (Generative) to work with our existing team of Data ... Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA · On-site +1

$125K - $160K/yr

Overview We are looking for seasoned Data Scientist to work with our existing team of Data ... Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA · On-site

$125K - $160K/yr

We are looking for a more than just a "Data Scientist", but a technologist with excellent ... Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA · On-site

$125K - $160K/yr

Overview We are looking for seasoned Data Scientist to work with our existing team of Data ... Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA · On-site

$125K - $160K/yr

We are looking for a more than just a "Data Scientist", but a technologist with excellent ... Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA

$125K - $160K/yr

We are looking for a more than just a "Data Scientist", but a technologist with excellent ... Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ...

Redhorse Corporation

Data Scientist

Arlington, VA · On-site +1

Data Scientist with 4 years of experience including experience in applied NLP, data labeling, entity or keyword extraction, and related topics. * Understanding of Weibull distribution and use for ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA · On-site

$125K - $160K/yr

We are looking for seasoned Data Scientist (Generative) to work with our existing team of Data ... Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ...

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Data Labeling information

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

To thrive in Data Labeling, you need meticulous attention to detail, strong analytical abilities, and basic computer literacy, often supported by a high school diploma or equivalent. Familiarity with data annotation tools, image or text editing software, and experience with platforms like Labelbox or Amazon SageMaker Ground Truth are commonly advantageous. Exceptional concentration, patience, and the ability to follow precise instructions are valuable soft skills in this position. These skills and qualities are essential for ensuring the accuracy and consistency of labeled datasets, which are critical for training reliable AI and machine learning models.

What is a Data Labeling job?

A Data Labeling job involves annotating or tagging data, such as images, text, audio, or videos, to help train machine learning models. Labelers follow specific guidelines to classify data accurately so that AI systems can learn patterns and make predictions. This role is essential in fields like computer vision, natural language processing, and speech recognition. Strong attention to detail and consistency are crucial for ensuring high-quality training datasets.

What are the typical day-to-day responsibilities of a Data Labeling professional?

A Data Labeling professional is primarily responsible for reviewing and accurately tagging images, text, audio, or video data according to specified guidelines. Daily tasks often include managing large datasets, using annotation software to classify data, and verifying the quality and accuracy of the labels. Collaboration with data scientists, project managers, and other annotators is common, especially when clarifying labeling guidelines or resolving ambiguities. Attention to detail is crucial, as high-quality labeled data directly impacts the effectiveness of machine learning models and AI applications. Most positions are structured in team environments, where productivity and communication skills help ensure project deadlines are met.

What is the job description of data labeling?

Data labeling involves annotating or tagging data such as images, text, or videos to help machine learning models understand and learn from the data. The role requires attention to detail, familiarity with labeling tools, and adherence to guidelines to ensure high-quality annotations. It is often performed remotely and may involve repetitive tasks with flexible schedules.

How much are data labelers paid?

Data labelers typically earn between $10 and $20 per hour, depending on experience, location, and the complexity of the labeling tasks. Many positions are freelance or remote, with pay rates varying across platforms and employers.

How do I become a data labeler?

To become a data labeler, you typically need basic computer skills, attention to detail, and the ability to follow instructions. Many positions require no formal degree and offer flexible, part-time schedules; familiarity with data annotation tools or platforms is often helpful. Applying through online job boards or company websites is common for entry-level roles.

Is data labelling a good career?

Data labeling is a common entry-level role in data annotation and machine learning workflows, often requiring attention to detail and basic computer skills. It can provide opportunities to develop skills in data management and AI, but typically offers lower pay and limited advancement without additional training or experience.
What are the most commonly searched types of Data Labeling jobs in Virginia? The most popular types of Data Labeling jobs in Virginia are:
What are popular job titles related to Data Labeling jobs in Virginia? For Data Labeling jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Data Labeling jobs in Virginia look for? The top searched job categories for Data Labeling jobs in Virginia are:
What cities in Virginia are hiring for Data Labeling jobs? Cities in Virginia with the most Data Labeling job openings:
Data Labeling jobs near you
Infographic showing various Data Labeling job openings in Virginia as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

RF Signals and Data Analyst

Quartermaster AI Inc

Arlington, VA • On-site

$160K - $210K/yr

Full-time

Re-posted 9 days ago

Job description

About Us:
At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.
Role Overview:
Quartermaster AI is seeking an experienced RF Signals Analyst with deep technical roots in communications and signals analysis and characterization to lead our signal characterization and data labeling efforts.
This role focuses on turning real world RF sensor data into structured ground truth for machine learning. You will analyze maritime RF events using spectrograms, waterfall plots, PSDs, metadata, and contextual sources like AIS and camera data when available. You will help define signals of interest, identify interference and host-platform noise, and label signals consistently for model development.
This is a hands-on technical role spanning RF analysis, data labeling, and ML dataset creation, with close collaboration across DSP and ML teams.
Key Responsibilities:
  • Analyze RF event data using IQ derived representations such as spectrograms, waterfall views, PSDs, and metadata to identify, classify, and tag signals of interest.
  • Help define and maintain a scalable maritime RF labeling taxonomy, including signal classes, confidence levels, rejection categories, and ambiguity handling.
  • Build and refine high quality labeled datasets for machine learning, ensuring labels are technically defensible, consistent, and auditable.
  • Identify and document recurring host vessel interference, platform artifacts, and environmental noise to support rejection library development.
  • Collaborate with DSP and ML engineers to review false positives, false negatives, and edge cases, and improve labeling standards over time.
  • Use available contextual data such as AIS, camera imagery, collection metadata, and sensor state to support signal interpretation when appropriate.
Qualifications:
  • 3+ years of experience in one or more of the following: RF signal analysis, SDR-based signal review, EW/SIGINT/ELINT analysis, RF dataset creation, or technical signal characterization.
  • Practical experience working with RF data products such as IQ captures, spectrograms, waterfall plots, PSDs, or other time frequency representations.
  • Experience working with structured labeling, annotation, classification, or technical review workflows where consistency and traceability matter.
  • Comfort working in a Linux-based environment using Python, SDR tools, notebooks, or other RF analysis environments to inspect, organize, and process signal data.
  • Ability to communicate clearly with engineers and translate signal observations into actionable labeling guidance.
  • Experience in maritime RF environments or other cluttered, interference heavy operational environments.
  • Understanding of how label quality, taxonomy design, multi-sensor context (for example AIS, EO/IR, or geolocation), and rejection categories affect downstream ML training and evaluation.
  • Active clearance or ability to obtain and maintain a Secret clearance.

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