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Ai Labelling Jobs in Reston, VA (NOW HIRING)

Steampunk

Data Scientist (Generative AI)

Mclean, VA · On-site

$125K - $160K/yr

Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ... You will contribute to the growth of our AI & Data Exploitation Practice! Qualifications ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA

$125K - $160K/yr

Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ... You will contribute to the growth of our AI & Data Exploitation Practice! Qualifications ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA

$125K - $160K/yr

Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ... You will contribute to the growth of our AI & Data Exploitation Practice! Qualifications * Ability ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA

$125K - $160K/yr

Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ... You will contribute to the growth of our AI & Data Exploitation Practice! Qualifications ...

Steampunk

Data Scientist (Generative AI)

Mclean, VA

$125K - $160K/yr

Identify, clean, label, and synthesize high-quality datasets for model training, fine-tuning, or ... You will contribute to the growth of our AI & Data Exploitation Practice! Qualifications * Ability ...

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How much do ai labelling jobs pay per hour?

As of May 29, 2026, the average hourly pay for ai labelling in Reston, VA is $56.62, according to ZipRecruiter salary data. Most workers in this role earn between $49.28 and $67.50 per hour, depending on experience, location, and employer.

What is an AI Labelling job?

An AI labelling job involves annotating data—such as images, text, audio, or video—to help train machine learning models. This process includes tasks like tagging objects in images, transcribing speech, or categorizing text. The labelled data is crucial for AI systems to learn and make accurate predictions. These jobs are commonly found in industries like tech, healthcare, and autonomous driving. Attention to detail and consistency are key skills for this role.

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

To thrive in an AI Labelling role, you need attention to detail, basic data analysis skills, and the ability to follow complex guidelines, with many roles requiring at least a high school diploma or equivalent. Familiarity with data annotation tools, image or text labeling platforms, and sometimes basic scripting or database systems is beneficial. Strong communication, time management, and the ability to work both independently and as part of a team are valuable soft skills. These competencies ensure the consistent and accurate labeling of data, which is critical for training high-quality AI and machine learning models.

What are some typical challenges faced in AI Labelling roles and how can they be managed?

One common challenge in AI Labelling roles is maintaining accuracy and consistency when labeling large volumes of data according to detailed guidelines, which can become repetitive or mentally taxing. Managing these challenges often involves taking regular breaks, double-checking work, and staying up-to-date with any updates to annotation standards provided by the team. Collaborating with supervisors and peers to clarify uncertainties and seek feedback also helps ensure high-quality output. Over time, professionals in this role often develop efficient workflows and a keen eye for detail, opening doors to advancement into quality assurance or project coordination positions within the data annotation field.
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Ai Labelling jobs near you

RF Signals and Data Analyst

Quartermaster AI Inc

Arlington, VA • On-site

Full-time

Posted 26 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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