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Explainable Ai Jobs in Virginia (NOW HIRING)

Seekr

Staff Solutions Architect (U.S. Government)

Reston, VA

$65.50 - $86.25/hr

Seekr is building trusted, explainable AI solutions for mission-critical environments. We are seeking a Solutions Architect to help U.S. Government customers understand and adopt our AI platform and ...

Leidos

Senior Data Scientist

Alexandria, VA · Hybrid

$131K - $237K/yr

Apply cutting-edge techniques in statistical analysis, predictive analytics, entity resolution, graph analytics, explainable AI, and operational analytics. * Work with diverse data types (structured ...

Leidos

Senior Software Engineer

Alexandria, VA · Hybrid

$131K - $237K/yr

Apply cutting-edge techniques in statistical analysis, predictive analytics, entity resolution, graph analytics, explainable AI, and operational analytics. * Contribute to the integration of software ...

NT Concepts

Data Scientist

Herndon, VA · On-site +1

Develop and visualize explainable AI metrics and model performance indicators * Incorporate research and development outputs into the operational code base * Communicate analytic findings to both ...

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Explainable Ai information

What is the difference between Explainable Ai vs Data Scientist?

AspectExplainable AiData Scientist
CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications like AI or ML courses are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentWorks within AI development teams, focusing on model transparency and interpretabilityWorks across data analysis, model building, and business insights, often in research or corporate settings
Industry UsageUsed in AI development, healthcare, finance, and any field requiring transparent AI modelsApplied in tech, finance, healthcare, and research for data-driven decision making

Explainable Ai focuses on making AI models transparent and understandable, ensuring trust and compliance. Data Scientists develop and analyze models, often working with complex data. While both roles involve AI and data, Explainable Ai specialists emphasize interpretability, whereas Data Scientists focus on model creation and insights.

What are some of the typical challenges faced when working in Explainable AI and how do professionals address them?

Professionals in Explainable AI often encounter challenges such as balancing model accuracy with interpretability, translating complex model outputs into understandable insights for non-technical stakeholders, and ensuring transparency without compromising sensitive data. Addressing these issues typically involves using specialized tools and frameworks for visualization, collaborating closely with data scientists, domain experts, and business teams, and staying updated on the latest research in model interpretability. Continuous learning and open communication are key to overcoming these challenges and delivering AI solutions that are both effective and trustworthy.

What are the key skills and qualifications needed to thrive as an Explainable AI specialist?

To thrive as an Explainable AI specialist, you need a strong background in machine learning, data science, and statistics, typically with an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and libraries like LIME or SHAP, as well as experience in model interpretability tools, is essential. Strong analytical thinking, effective communication, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These capabilities ensure that AI models are transparent, trustworthy, and can be responsibly integrated into decision-making processes.

What is Explainable AI?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable and interpretable by humans. XAI aims to provide transparency into how AI systems make decisions, helping users trust and effectively manage AI applications. This is especially important in fields like healthcare, finance, and law, where understanding the reasoning behind AI-driven outcomes can be crucial for accountability and compliance. By making AI more transparent, XAI also helps identify and address biases or errors in AI systems.
What are popular job titles related to Explainable Ai jobs in Virginia? For Explainable Ai jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Explainable Ai jobs in Virginia look for? The top searched job categories for Explainable Ai jobs in Virginia are:
What cities in Virginia are hiring for Explainable Ai jobs? Cities in Virginia with the most Explainable Ai job openings:
Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in Virginia as of August 2026, with employment types broken down into 48% Full Time, and 52% Contract. Highlights an 82% In-person, and 18% Remote job distribution.

Data Scientist / AI/ML Engineer (Imagery) VAWFH 1652

Global InfoTek, Inc.

Reston, VA • Remote

$100K - $250K/yr

Full-time

Re-posted 24 days ago

Job description


Clearance Level: TS/SCI

US Citizenship: Required

Job Classification: Regular, Full-Time

Salary: $235K - $275K

Location: Reston, VA
Years of Experience: 5-7 Years

Education Level: Bachelor degree or Master's degree is required in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Physics, Computer Science, or related fields.

Briefly Describe the Work:

GITI is looking for a Machine Learning (ML) Engineer with documented expertise to be responsible for researching, developing, architecting, and integrating ML models, algorithms, tools, and techniques into existing or new environments. A candidate who has experience analyzing large datasets with preprocessing data skills, data cleansing, and conducting data integrity and validation actions. A candidate who will design, develop, and integrate ML models and algorithms to address specific problems with detection of AI-generated and manipulated imagery, or introduction of pattern recognition for imagery. Successful candidates for this role must have critical thinking skills, be creative, curious, resourceful, and have a passion for conveying a wide range of information through research leading to deeper insights. The candidate may work independently but participate in project-wide reviews of requirements, system architecture, and detailed design documents. An ML Engineer must be able to collaborate well with a strong lean-forward attitude to shift knowledge left, deliver well, and produce quality results. The selected candidate will play a critical role in bridging research and operations, integrating advanced algorithms into operational workflows.

  • Integrate and operationalize machine learning and computer vision models developed by research partners
  • Apply algorithms to datasets and generate results aligned with project requirements
  • Evaluate model performance using metrics such as accuracy, precision, recall, ROC/AUC, and localization quality
  • Adapt and optimize models for real-world conditions (compression, noise, format variability)
  • Work with containerized solutions (Docker or similar) to support deployment and reproducibility
  • Translate research concepts, theory, and technical reports into practical implementations and workflows
  • Generate technical reports, visualizations, and summaries suitable for stakeholders
  • Collaborate with internal teams and external partners to support integration and testing
  • Participate in technical meetings and reviews within secure environments. Candidates must have a complete understanding and wide application of technical principles, theories and concepts. Working under only general direction, provides technical solutions to a wide range of difficult problems. Independently determines and develops approach to solutions.

Required Skills:

  • 3+ years of experience in machine learning / computer vision / data science
  • Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow)
  • Experience working with image and/or video data
  • Ability to understand and implement research-level algorithms from technical papers and reports
  • Experience with model evaluation, validation, and performance analysis
  • Familiarity with Linux-based environments and version control systems (e.g., Git)

Desired Skills:

  • Experience with image forensics, alteration detection, or related analytics
  • Experience working in classified environments
  • Familiarity with containerization (Docker) and deployment pipelines
  • Experience handling large-scale or multi-format datasets (JPG, WEBP, MP4, AVI)
  • Knowledge of synthetic data generation or explainable AI
  • Ability to bridge theory and implementation
  • Strong problem-solving and analytical skills
  • Effective communication with both technical and non-technical stakeholders
  • Comfortable working in structured, mission-driven environments

Relevant Certifications:

  • Certifications in machine learning, data science, or related fields (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, IBM Machine Learning Professional Certificate, Google Professional Machine Learning Engineer Certification, IABAC: Certified Machine Learning Expert Certification, 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 based on 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.

Employment Type: FULL_TIME

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