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Artificial Intelligence Explainable Jobs (NOW HIRING)

ADP, Inc.

$200 - $250/hr

... Artificial Intelligence) in our Plymouth Meeting location . The R&D & AI business Technology ... explainable and auditable outcomes. The R&D & AI Business Technology Partner role is a hands‑on ...

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How much do artificial intelligence explainable jobs pay per year?

As of Sep 9, 2026, the average yearly pay for artificial intelligence explainable in the United States is $102,938.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,000.00 and $132,500.00 per year, depending on experience, location, and employer.

What is artificial intelligence explainable?

Artificial Intelligence Explainables, often referred to as Explainable AI (XAI), are methods and techniques that make the results and processes of AI systems understandable to humans. XAI helps users and stakeholders comprehend how AI models make their decisions, increasing trust and transparency. This is especially important in critical fields like healthcare, finance, and law, where understanding AI reasoning can impact lives or regulations. By providing clear explanations, XAI also helps developers identify potential biases or errors in AI models.

What are the key skills and qualifications needed to thrive as an artificial intelligence explainability specialist?

To thrive as an Artificial Intelligence Explainability Specialist, you need a strong background in machine learning, statistics, and data science, typically supported by a degree in computer science or a related field. Familiarity with explainability frameworks (such as LIME, SHAP, or Fairness Indicators), programming languages like Python, and cloud-based AI platforms is essential. Critical thinking, clear communication, and the ability to translate complex technical concepts to non-technical stakeholders are standout soft skills. These skills ensure that AI models are transparent, trustworthy, and can be effectively adopted in real-world applications where accountability and compliance are crucial.

What are some common challenges faced by professionals working in artificial intelligence explainability, and how can they be addressed?

Professionals in Artificial Intelligence Explainability often encounter challenges such as translating complex AI model decisions into transparent, user-friendly explanations and ensuring those explanations are understandable to non-technical stakeholders. Additionally, balancing the trade-off between model accuracy and interpretability can be difficult, as more interpretable models sometimes sacrifice predictive performance. To address these challenges, practitioners typically use a combination of model-agnostic explainability tools, collaborate closely with data scientists and domain experts, and prioritize clear communication with end users. Ongoing learning and staying up-to-date with the latest research in explainable AI also help in overcoming these hurdles.

What is the difference between Artificial Intelligence Explainable vs Data Scientist?

AspectArtificial Intelligence ExplainableData 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 works in AI development teams, research labs, or tech companies focusing on explainability of AI modelsWorks in data analysis, modeling, and insights generation across various industries like finance, healthcare, and tech
Industry UsageUsed in AI model development, especially for transparent and interpretable AI systemsApplied in data analysis, predictive modeling, and business intelligence

In summary, Artificial Intelligence Explainable specialists focus on making AI models transparent and understandable, often working closely with AI development teams. Data Scientists analyze data and build models but may not specialize in explainability. Both roles require strong analytical skills and knowledge of machine learning, but their primary focus and work environments differ.

What other helpful pages are available for Artificial Intelligence Explainable?

Other pages related to Artificial Intelligence Explainable:

Infographic showing various Artificial Intelligence Explainable job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 87% Full Time, 9% Part Time, and 3% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution, with an average salary of $102,938 per year, or $49.5 per hour.

Postdoctoral Scholar in Health AI-Pediatrics CBMI

Memphis, TN • On-site

The University of Tennessee
Colleges, Universities, and Professional Schools • 1 - 5K employees
Learn more about The University of Tennesseeexternal

Full-time

Re-posted 13 days ago

Job description


THIS IS A GRANT-FUNDED POSITION FUNDED UNTIL OCTOBER 1, 2027.
The Postdoctoral Scholar designs, develops, evaluates, and optimizes advanced artificial intelligence (AI) methods that support intelligent cancer patient navigation and clinical decision support. Under the direction of the Principal Investigator, this position leads research and development on multimodal machine learning, agentic AI, explainable AI, causal inference, predictive analytics, and continuous learning to develop trustworthy AI solutions that improve cancer care delivery.
Responsibilities
  1. Designs, develops, implements, and optimizes advanced artificial intelligence, machine learning, and multimodal AI models for intelligent cancer patient navigation and clinical decision support.
  2. Conducts research in explainable AI, agentic AI, causal inference, predictive analytics, knowledge representation, and continuous learning.
  3. Designs and evaluates AI algorithms using electronic health records, patient-reported outcomes, social determinants of health, medical imaging, and other healthcare data sources.
  4. Conducts benchmarking, validation, performance evaluation, and fairness, robustness, and explainability assessments of AI model.
  5. Collaborates with software engineers, clinicians, and interdisciplinary investigators to translate AI research into interoperable clinical applications and decision support tools.
  6. Develops and maintains reproducible analytical workflows and research software to support AI model development and evaluation
  7. Mentors graduate students and junior researchers throughout the project lifecycle.
  8. Prepares manuscripts, technical reports, conference presentations, and publications in leading journals and scientific meetings.
  9. Participates in proposal preparation and collaborative research activities supporting federally and state-funded research programs.
  10. Performs other duties as assigned.

Qualifications
Ph.D. in a relevant discipline (e.g. Medical Informatics, Computer Science, Software Engineering, Mathematics, etc.)
Track record of publications in top journals and conferences in the field. Strong track record of quantitative and analytics mastery, and expertise in Artificial Intelligence, Machine Learning, Causal Modeling, and Knowledge Graphs. Strong coding and implementation skills. Outstanding interpersonal skills and written and verbal communication capabilities.
WORK SCHEDULE: This position may occasionally be required to work weekends and evenings. May require occasional overnight travel.

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