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Explainable Ai Xai Jobs (NOW HIRING)

InstantServe LLC

Data Scientist

Apply explainable AI (XAI) techniques and Responsible AI frameworks (NIST AI RMF) * Deliver secure solutions in AWS GovCloud or Azure FedRAMP environments * Support ATO processes and ensure ...

ClifyX

GenAI Engineer

Wilmington, DE · On-site

... explainable AI (XAI) for transparent decision-making • Optimize cost, latency, and scalability of AI systems • Troubleshoot AI/ML system issues across data and deployment layers • Write ...

RIT Solutions

Integration Architect

$72.50 - $93.50/hr

Experience with explainable AI (XAI) techniques. * Experience with MLOps and model deployment pipelines. * Experience with containerization technologies (e.g., Docker, Kubernetes). * Experience with ...

JP Morgan Chase

CCB Risk Program senior Associate

Plano, TX · On-site

$56K - $56K/yr

The CCB Risk Modeling team i s seeking talented professionals with expertise in machine learning, explainable AI (XAI), and responsible AI practices, with a focus on credit decision and fraud ...

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Explainable Ai Xai information

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$46.5K

$94.5K

$142K

How much do explainable ai xai jobs pay per year?

As of Jun 29, 2026, the average yearly pay for explainable ai xai in the United States is $94,542.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,000.00 and $95,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in Explainable AI (XAI) roles?

Professionals in Explainable AI often encounter the challenge of balancing model accuracy with interpretability, as more complex models can be harder to explain. Another common difficulty is effectively communicating technical findings to non-technical stakeholders, ensuring that explanations are both accurate and accessible. Additionally, XAI specialists must stay updated with rapidly evolving regulations and best practices, as transparency requirements continue to grow in industries like finance and healthcare. Collaboration with data scientists, product managers, and compliance teams is also a regular part of the role, requiring strong interdisciplinary communication skills.

What is Explainable AI (XAI)?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the decisions and outputs of AI systems understandable and interpretable to humans. Unlike traditional 'black box' AI models, XAI aims to provide clear explanations about how and why a particular decision was made by the system. This transparency is crucial in fields like healthcare, finance, and legal, where understanding the reasoning behind AI decisions is essential for trust, accountability, and compliance. XAI can use various approaches, such as visualizations, simplified models, or feature importance scores, to explain predictions in a user-friendly way.

What are the key skills and qualifications needed to thrive as an Explainable AI (XAI) Specialist, and why are they important?

To thrive as an Explainable AI (XAI) Specialist, you need a strong background in machine learning, statistics, and computer science, typically supported by an advanced degree in a related field. Familiarity with technical tools such as Python, TensorFlow, and specialized XAI libraries like LIME or SHAP, as well as knowledge of regulatory standards, is essential. Strong communication skills, critical thinking, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These abilities ensure that AI systems are transparent, trustworthy, and ethically aligned with business and regulatory requirements.

What is the difference between Explainable Ai Xai vs Data Scientist?

AspectExplainable Ai XaiData Scientist
Required CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications in AI or data science are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentOften in AI development teams, focusing on model transparency and interpretabilityIn research, analytics, or product teams, focusing on data modeling, analysis, and insights
Industry UsageUsed across tech, finance, healthcare for AI transparency and complianceUsed across industries for data analysis, predictive modeling, and decision support

Explainable Ai Xai focuses on making AI models transparent and understandable, often working closely with AI development teams. Data Scientists analyze data, build models, and generate insights. While both roles involve data and machine learning, Xai emphasizes model interpretability, whereas Data Scientists focus on data analysis and predictive modeling.

Infographic showing various Explainable Ai Xai job openings in the United States as of June 2026, with employment types broken down into 80% Full Time, 7% Part Time, and 13% Contract. Highlights an 93% In-person, and 7% Remote job distribution, with an average salary of $94,542 per year, or $45.5 per hour.

Senior AI/ML Modeling, Simulation & Analysis Engineer (Part-Time)

TRIAEM, LLC

Sterling, VA

$105K - $144K/yr

Other

Posted 29 days ago

Job description

Senior AI/ML Modeling, Simulation & Analysis Engineer (Part-Time)

Active TS/SCI Required
Location: Chantilly, Va or Springfield, Va

We are seeking a Senior AI/ML Modeling, Simulation & Analysis (MS&A) Engineer to support NGA mission initiatives focused on AI modernization, model assurance, and verification frameworks. This part-time role provides high-level technical advisory support across AI/ML strategy, verification, validation, and explainability efforts in classified GEOINT environments.

This is a senior-level opportunity for an experienced engineer who understands both the operational mission and the rigor required for trusted AI systems in national security applications.

Key Responsibilities

  • Conduct AI/ML gap analyses assessing impact across intelligence, command and control, targeting, autonomous systems, and training environments.
  • Provide subject matter expertise in AI Assurance, Independent Verification & Validation (IV&V), and Explainable AI (XAI).
  • Support Analysis of Alternatives (AoA), Design of Experiments (DoE), trade studies, and engineering assessments.
  • Advise Government stakeholders on strategic technical planning, performance engineering, and risk management.
  • Develop quantitative and qualitative metrics to ensure confidence and trust in AI models.
  • Contribute to AI Assurance frameworks including V&V-as-a-Service and advanced T&E methodologies.
  • Evaluate system architectures, cloud capabilities, CI/CD pipelines, and automation workflows.
  • Collaborate across NGA, NSG, IC partners, and industry teams to advance enterprise AI strategy.

Required Qualifications

  • Active TS/SCI clearance (required).
  • Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or related STEM field (or equivalent senior-level experience).
  • Demonstrated experience in AI model verification, validation, and evaluation (IV&V / T&E).
  • Experience supporting AI Assurance and/or Explainable AI (XAI) initiatives.
  • Proficiency in programming languages such as Python, C++, MATLAB, R, or Java.
  • Experience developing engineering solutions leveraging AI, automation, and augmentation technologies.

Desired Qualifications

  • Experience supporting NGA or Intelligence Community programs.
  • Familiarity with DevSecOps, CI/CD pipelines, and cloud-based AI environments.
  • SAFe Agile and/or Model-Based Systems Engineering (MBSE).
  • Experience with Atlassian tools (JIRA, Confluence).
  • Experience working with structured and unstructured Big Data.

Apply