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Full Time Ai Data Annotation Jobs in Virginia (NOW HIRING)

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Full Time Ai Data Annotation information

What are some common challenges faced by full time AI data annotation professionals, and how can they be addressed?

AI Data Annotation professionals often encounter challenges such as maintaining high accuracy while working with large datasets, interpreting ambiguous data, and consistently following complex labeling guidelines. These challenges can be addressed through thorough training, frequent communication with project managers or data scientists, and utilizing annotation tools with built-in quality checks. Collaboration with team members and regular feedback sessions also help ensure consistency and improve overall data quality, making the annotation process smoother and more efficient.

What are the key skills and qualifications needed to thrive as a full time AI data annotation specialist?

To thrive as a Full Time AI Data Annotation Specialist, you need strong attention to detail, basic data literacy, and often a high school diploma or equivalent. Familiarity with annotation platforms (like Labelbox or Supervisely) and understanding of data labeling guidelines are typically required. Patience, consistency, and effective communication are soft skills that help ensure accuracy and clarity in collaborative projects. These skills and qualities are crucial for producing high-quality labeled data, which directly impacts the performance of AI models.

What is a full time AI data annotation job?

Full Time AI Data Annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Annotators play a crucial role in ensuring AI systems understand and process information accurately by providing high-quality, human-curated data. These positions usually require attention to detail, basic computer skills, and the ability to follow specific guidelines for different projects. Full-time roles typically offer stable hours and may be remote or on-site, depending on the employer.

What is the difference between Full Time Ai Data Annotation vs Data Labeler?

AspectFull Time Ai Data AnnotationData Labeler
CredentialsHigh school diploma or equivalent; some roles prefer basic technical skillsHigh school diploma or equivalent; minimal technical requirements
Work EnvironmentOffice or remote; part of AI development teamsOffice or remote; often task-based or freelance
Industry UsageUsed across AI, machine learning, and data science industriesPrimarily in AI and machine learning industries for data preparation
Job ScopeFull-time, with responsibilities including data annotation, quality control, and collaborationTask-specific, focusing on labeling data accurately for AI training

Full Time Ai Data Annotation roles typically require more consistent hours, team collaboration, and a broader scope of responsibilities compared to Data Labelers, who often work on individual tasks with minimal oversight. Both roles are essential in AI development, but Full Time Ai Data Annotation offers more stability and integration within AI projects.

What are the most commonly searched types of Ai Data Annotation jobs in Virginia? The most popular types of Ai Data Annotation jobs in Virginia are:
What are popular job titles related to Full Time Ai Data Annotation jobs in Virginia? For Full Time Ai Data Annotation jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Full Time Ai Data Annotation jobs in Virginia look for? The top searched job categories for Full Time Ai Data Annotation jobs in Virginia are:
Full Time Ai Data Annotation jobs near you
Infographic showing various Full Time Ai Data Annotation job openings in Virginia as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Transportation Data Scientist (AI Solutions)

Leidos

Mclean, VA • On-site

Full-time

Re-posted 11 days ago

Leidos rating

8.3

Company rating: 8.3 out of 10

Based on 151 frontline employees who took The Breakroom Quiz

77th of 485 rated business services

Job description

Are you interested in improving and shaping the transportation industry in a group of intelligent and motivated individuals? Leidos operates the Federal Highway Administration's (FHWA) Saxton Transportation Operations Laboratory (STOL), a USDOT research lab focused on the improvement of transportation operations, safety, mobility, and environmental impacts. STOL provides a variety of services to support the advancement and deployment of emerging technologies, including vehicle automation and communication.
Leidos is seeking a talented Transportation Data Scientist at the junior to mid-level to support FHWA-funded projects at the intersection of AI, data science, and transportation. This role will involve assisting in the development and deployment of AI/ML models for applications such as vehicle load classification using weigh-in-motion (WIM) data and imagery, crash prediction in traffic management centers (TMCs), and creating data ecosystems for trustworthy AI. The ideal candidate will have foundational experience in AI model development, data integration, and stakeholder engagement, with a passion for exploring opportunities to apply AI in state-level transportation initiatives. This position offers the chance to contribute to innovation in a dynamic, federally supported research environment.
Location: This role will be expected to work full-time at the customer site in McLean, VA
Candidate MUST:
Be currently located in the United States for the current three consecutive years and be eligible for a Public Trust Clearance.
https://careers.leidos.com/search/jobs?q=stol&ns_job_category=stol-jobs
Primary Responsibilities
• Assist in conducting data and literature reviews, including targeted searches for AI methods, datasets, and technologies relevant to freight analytics, traffic safety, and operations (e.g., sensor fusion, computer vision, and multimodal AI).
• Prepare and integrate datasets for AI use cases, including cleaning, normalizing, enriching, and fusing multi-source data (e.g., traffic logs, imagery, weather, and permitting records) while addressing quality issues like inconsistency, sparsity, and bias.
• Contribute to the design, development, and deployment of AI/ML models for transportation applications.
• Evaluate AI model performance under diverse conditions, such as varying data quality levels, and provide recommendations for improving model robustness, scalability, and trustworthiness in real-world transportation environments.
• Support stakeholder outreach and engagement, including organizing peer exchanges, workshops, and technical briefings with state DOTs, MPOs, enforcement agencies, and vendors to gather insights on AI applications.
• Collaborate with cross-functional teams to ensure project alignment with USDOT goals, including risk management, quality assurance, and compliance with federal standards.
• Contribute to monthly progress reporting, risk mitigation, and iterative model refinement based on federal feedback.
Required Qualifications
  • Master's degree in computer science, Data Science, Artificial Intelligence, Transportation Engineering, or a related field; Ph.D. preferred.
  • 2+ years of professional experience (NON-academic) in data science and AI/ML, with demonstrated familiarity in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn), data processing tools (e.g., Pandas, NumPy), and AI techniques (e.g., deep learning, generative AI like GANs, computer vision, LLMs).
  • Must have HANDS-ON experience in AI Solution Development.
  • Strong experience in data preparation and integration, including ETL processes, handling multimodal data (e.g., imagery, sensor data, time-series), and addressing data quality challenges in real-world applications.
  • Strong analytical skills with familiarity in model evaluation metrics (e.g., AUC, accuracy, scalability) and testing AI systems under varied conditions.
  • Excellent communication and collaboration skills, with experience in stakeholder engagement, technical reporting, and presenting complex AI concepts to non-technical audiences.
  • Ability to work in a fast-paced, research-oriented environment with travel up to 20% for stakeholder meetings, site visits, or conference support.
  • Ability to obtain and maintain a Public Trust clearance (which includes three years of immediate residency in the US).
  • All applicants must be legally authorized to work in the United States.

Preferred Qualifications
  • Prior experience working with state DOTs or federal transportation agencies (e.g., FHWA, USDOT) on AI initiatives, including prototyping and developing AI application in ITS.
  • Familiarity with transportation-specific data sources (e.g., HSIS, SHRP2, NGSIM) and standards (e.g., SAE J2735 for V2X).
  • Experience in synthetic data generation, generative AI (e.g., LLMs), or physics-informed ML for transportation applications.
  • Knowledge of federal AI governance, risk management, and equity considerations in transportation.
  • Project management experience, including leading AI tasks in multi-agency initiatives or contributing to communities of practice (CoPs).
  • Publications or presentations in AI/transportation conferences (e.g., TRB, ITS America).

Anticipated salary range for this role is $100,000-$120,000
If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares.
Original Posting:
June 26, 2026
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:
Pay Range $87,100.00 - $157,450.00
The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.

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About Leidos

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At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable practices. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Reston, VA, US

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