ZipRecruiter

Log In

1

Data Labelling Jobs in Silver Spring, MD (NOW HIRING)

next page

Showing results 1-20

All JobsData Labelling JobsData Labelling Jobs in Silver Spring, MD

Data Labelling information

See Silver Spring, MD salary details

$47.6K

$170.6K

$251.7K

How much do data labelling jobs pay per year?

As of Jun 20, 2026, the average yearly pay for data labelling in Silver Spring, MD is $170,592.00, according to ZipRecruiter salary data. Most workers in this role earn between $138,000.00 and $175,700.00 per year, depending on experience, location, and employer.

What is a Data Labelling job?

A Data Labelling job involves annotating data, such as text, images, audio, or video, to help train machine learning models. Labelers categorize or tag data by following specific guidelines to ensure accuracy and consistency. This process is essential for improving AI applications, including image recognition, natural language processing, and autonomous systems. Attention to detail and adherence to instructions are key skills required for this role.

What are the typical daily responsibilities of a Data Labelling professional?

Data Labelling professionals are generally responsible for reviewing and accurately annotating large volumes of data—such as images, audio, video, or text—to support machine learning and AI projects. This often involves using specialized labeling platforms and following detailed guidelines provided by data scientists or project managers. You may also participate in regular team meetings to discuss quality standards or address ambiguities in data, and your work is typically reviewed for accuracy before being integrated into training datasets. Collaborating with other data annotators, engineers, and analysts is a common part of the process to ensure consistency and high-quality results.

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

To thrive as a Data Labelling professional, you need strong attention to detail, proficiency with data annotation processes, and a basic understanding of machine learning concepts. Familiarity with annotation tools like Labelbox, Supervisely, or Amazon SageMaker Ground Truth is often required, and some roles may value certifications in data processing or AI fundamentals. Reliability, patience, and the ability to follow precise instructions are important soft skills for success in this position. These skills ensure accurate and consistent data labeling, which is critical for developing effective AI models and maintaining data integrity.

What are popular job titles related to Data Labelling jobs in Silver Spring, MD? For Data Labelling jobs in Silver Spring, MD, the most frequently searched job titles are:
What job categories do people searching Data Labelling jobs in Silver Spring, MD look for? The top searched job categories for Data Labelling jobs in Silver Spring, MD are:
What cities near Silver Spring, MD are hiring for Data Labelling jobs? Cities near Silver Spring, MD with the most Data Labelling job openings:
Data Labelling jobs near you

RF Signals and Data Analyst

Quartermaster AI Inc

Arlington, VA • On-site

Full-time

Posted 17 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.

Apply