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Remote Data Labeling Jobs in Washington, DC (NOW HIRING)

Humana

Senior Insider Threat Engineer

Washington, DC · On-site +1

$129K - $177K/yr

Become a part of our caring community (Remote role, though selected candidate is required to live ... Working knowledge of insider threat tactics, techniques, and procedures (TTPs), including data ...

New

Humana

Senior Insider Threat Engineer

Washington, DC · On-site +1

$129K - $177K/yr

Become a part of our caring community (Remote role, though selected candidate is required to live ... Working knowledge of insider threat tactics, techniques, and procedures (TTPs), including data ...

New

GRVTY

Imagery Scientist (EO)- Expert

Falls Church, VA · On-site +1

$180K - $210K/yr

Data formats * APIs and ETL processes * Existing operational pipelines * Develop strategies to ... Labeling * Model testing * Advanced exploitation workflows * Investigate gaps in emerging EO sensor ...

Humana

Senior Insider Threat Engineer

Washington, DC · On-site +1

$129K - $177K/yr

Become a part of our caring community (Remote role, though selected candidate is required to live ... Working knowledge of insider threat tactics, techniques, and procedures (TTPs), including data ...

New

Booz Allen Hamilton, Inc.

Network Engineer

Lorton, VA · On-site +1

$62K - $141K/yr

Remote Work: No Job Number: R0242179 Location: Lorton,VA,US Share job via: Share Network Engineer ... A well-designed network is critical to move data and enable the USG and commercial partners to ...

Aprio

Lead Engineer (Azure)

Washington, DC · On-site +1

$98K - $132K/yr

Data Protection : Purview, DLP, Sensitivity Labels, DSPM * Cloud Security : Azure Defender for ... remote and hybrid options What's in it for you: - Working with an industry leader : Be part of a ...

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All JobsData Labeling JobsRemote Data Labeling JobsRemote Data Labeling Jobs in Washington, DC

Remote Data Labeling information

What are common challenges faced by remote data labelers, and how can they be managed?

Remote data labelers often face challenges such as maintaining focus during repetitive tasks, managing volume-based workloads, and interpreting ambiguous data with consistency. To manage these, it's important to set up a distraction-free workspace, take regular breaks to avoid fatigue, and seek clarification from supervisors or project guidelines when uncertainties arise. Most companies provide onboarding and ongoing support to help new labelers understand annotation standards and best practices. Collaborating with remote team members via chat or project management platforms also helps maintain quality and stay connected. By being proactive and utilizing available resources, remote data labelers can maintain high accuracy and productivity.

What skills and qualifications are needed for remote data labeling?

To thrive as a Remote Data Labeling specialist, you need strong attention to detail, basic data analysis skills, and the ability to accurately tag and categorize diverse data types, often with a high school diploma or equivalent. Familiarity with data labeling platforms, annotation tools (such as Labelbox or Amazon SageMaker Ground Truth), and, occasionally, basic knowledge of data privacy standards is helpful. Time management, self-discipline, and effective remote communication are valuable soft skills in this position. These skills ensure that labeled data is accurate and reliable, supporting the success of machine learning and AI projects.

What is remote data labeling?

A Remote Data Labeling job involves annotating or categorizing data, such as images, text, audio, or video, to train machine learning models. Workers review and tag content based on specific guidelines provided by companies. This job is typically done online from home and requires attention to detail, consistency, and sometimes specialized domain knowledge. It plays a crucial role in improving artificial intelligence systems by providing high-quality labeled data.

What are popular job titles related to Remote Data Labeling jobs in Washington, DC? For Remote Data Labeling jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Remote Data Labeling jobs in Washington, DC look for? The top searched job categories for Remote Data Labeling jobs in Washington, DC are:
Remote Data Labeling jobs near you
Infographic showing various Remote Data Labeling job openings in Washington, DC as of August 2026, with employment types broken down into 100% Part Time. Highlights an 100% Remote job distribution.

AI/ML Engineer, Senior - WFH1659

Global InfoTek, Inc.

Reston, VA • On-site, Remote

$150 - $200/hr

Full-time

Re-posted 7 days ago

Job description

Clearance Level: Public Trust
US Citizenship: Required
Job Classification: 1099/Contractor ($150 - $200 per hour)
Location: Remote
Years of Experience: 5-7 years of relevant experience
Education Level: BS or MS in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related quantitative field. Experience may be considered in place of education requirement.
Briefly Describe the Work:
GITI is seeking a Senior AI/ML Engineer to support an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Senior AI/ML Engineer designs, builds, and validates machine learning models for RF emitter identification, conducts hands-on exploratory data analysis on NDF (Network Description File) sensor datasets, and implements ML data pipelines that operate on constrained tactical edge hardware. Working under the direction of the Principal AI/ML Engineer and program technical lead, the candidate collaborates closely with research scientists and software engineers to translate analytical findings into reproducible, well-documented ML experiments and pipeline components. The role requires strong Python and deep learning skills, comfort with real-world noisy sensor data, and the ability to work in air-gapped Linux environments without cloud infrastructure or GPU acceleration.
Responsibilities:
  • Design, build, and validate machine learning models for RF emitter identification - including feature engineering from sensor data, training pipeline development, model evaluation, and iterative refinement based on results
  • Conduct hands-on exploratory data analysis on RF sensor datasets using Python and Jupyter notebooks - writing and running analytical code, characterizing feature distributions, identifying data quality issues, and producing documented findings
  • Implement and maintain ML data pipelines - ingesting NDF sensor streams, applying rollup and preprocessing logic, constructing training datasets, and ensuring pipeline correctness on constrained edge hardware with no cloud dependency
  • Collaborate with the technical lead and Principal AI/ML Engineer to investigate RF sensor data quality, attribution reliability, and feature behavior under contention - writing code to characterize error sources, validate assumptions, and reproduce findings
  • Produce clear technical documentation of experiments, model configurations, and results - maintaining reproducibility through disciplined versioning, and contributing to monthly status reports and team knowledge sharing

Career level with a complete understanding and wide application of machine learning principles and data science techniques. Working under general direction from the Principal AI/ML Engineer, executes independently on assigned modeling and analysis tasks, contributes to pipeline development, and produces reproducible, well-documented results. Bachelor's or Master's (or equivalent) with 5-7 years of hands-on applied experience.
Required Skills:
  • 5+ years of hands-on applied experience in machine learning, data science, or RF signal processing
  • Demonstrated proficiency in Python for ML and data science work - PyTorch or TensorFlow for model development, Pandas/NumPy for data manipulation, and scikit-learn or similar for evaluation and baseline modeling
  • Hands-on experience designing, training, and evaluating deep learning models - particularly metric learning, Siamese networks, or other similarity-learning architectures - on real-world, noisy, imbalanced datasets
  • Practical experience handling real-world data quality problems - missing values, label noise, class imbalance, systematic bias, and sensor artifacts - and the ability to diagnose and address them without discarding valid data
  • Ability to develop and run ML pipelines on Linux-based systems without cloud infrastructure or GPU acceleration - optimizing for CPU-only inference and multi-threaded data processing on resource-constrained x86 hardware

Desired Skills:
  • Familiarity with RF signal characteristics, passive receiver phenomenology, and sensor data interpretation - including awareness of processing artifacts, attribution ambiguities, and measurement limits common in signals intelligence datasets
  • Hands-on experience applying machine learning - particularly metric learning, deep learning networks, or similarity-learning architectures - to RF or time-series signal data, including feature engineering, training pipeline development, and model validation
  • Exposure to TDMA network protocols or military datalink systems, and interest in learning the signal processing challenges of dense, contested electromagnetic environments
  • Familiarity with direction-finding, time-difference-of-arrival (TDOA), or related passive geolocation concepts - understanding of their mathematical foundations and common failure modes is more important than operational experience
  • Experience with binary serialization formats (FlatBuffers, Protocol Buffers) and high-throughput sensor data pipelines operating in near-real-time on resource-constrained hardware
  • Background in statistical signal processing - error ellipses, bearing estimation uncertainty, feature reliability under noise - with the ability to distinguish statistically significant findings from artifacts of small sample size or improper normalization

Relevant Certifications:
  • Certifications in machine learning, data science, or related technical fields (e.g., TensorFlow Developer Certificate; PyTorch Certified Associate; AWS Certified Machine Learning - Specialty; Microsoft Certified: Azure AI Engineer Associate; Certified Analytics Professional (CAP); etc.)

Global InfoTek, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.
About Global InfoTek, Inc. Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation's pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.

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