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Ai Labelling Jobs in Virginia (NOW HIRING)

Dev Technology

Audio AI Engineer

Reston, VA

$80K - $160K/yr

Audio AI Engineer, #1085 Multilingual Speech-to-Text Engineer - On-Device Model Optimization, #1085 ... Ingest, clean, segment, label, and version multilingual audio and transcript data, with attention ...

New

CBRE

Union Data Center Installer

Boydton, VA · On-site

Affixing Labels to cabling and Power Cords * Affix bar coding to equipment * Affix free printed ... Applicant AI Use Disclosure: We value human interaction to understand each candidate's unique ...

Dev Technology

Audio AI Engineer

Reston, VA

$80K - $160K/yr

Audio AI Engineer, #1085 Multilingual Speech-to-Text Engineer -- On-Device Model Optimization ... Ingest, clean, segment, label, and version multilingual audio and transcript data, with attention ...

New

Dev Technology

Audio AI Engineer

Reston, VA · On-site

$80K - $160K/yr

... AI Engineer, #1085 Multilingual Speech-to-Text Engineer - On-Device Model Optimization, #1085 A ... Ingest, clean, segment, label, and version multilingual audio and transcript data, with attention ...

New

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Ai Labelling information

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.

What is an AI labelling?

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 AI labelling?

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 popular job titles related to Ai Labelling jobs in Virginia? For Ai Labelling jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Ai Labelling jobs in Virginia look for? The top searched job categories for Ai Labelling jobs in Virginia are:
What cities in Virginia are hiring for Ai Labelling jobs? Cities in Virginia with the most Ai Labelling job openings:
Ai Labelling jobs near you
Infographic showing various Ai Labelling job openings in Virginia as of August 2026, with employment types broken down into 73% Full Time, 23% Part Time, and 4% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution.

AI/ML Engineer, Senior - WFH1659

Global InfoTek Inc

Reston, VA • On-site

$150 - $200/hr

Temporary

Re-posted 8 days ago

Job description

Clearance Level: Public Trust (Secret Eligible)

US Citizenship: Required

Job Classification: 1099/Consultant

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.