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Explainable Ai Jobs (NOW HIRING)

Brain Gain Recruiting

Explainable AI Engineer

Palo Alto, CA · Remote

$122K - $165K/yr

We're hiring an Explainable AI Engineer to build, verify, and validate business impact predictions based on outputs of our AI Engines. You'll have an opportunity to learn from Stanford and Georgia ...

LLNL

Explainable AI - Postdoctoral Researcher

Livermore, CA · On-site

$136K/yr

We have an opening for a Postdoctoral Researcher in Explainable AI to contribute to fundamental R&D on understanding what modern AI models learn and how that knowledge is represented internally. As ...

LLNL

Explainable AI - Postdoctoral Researcher

Livermore, CA · On-site

$136K/yr

We have an opening for a Postdoctoral Researcher in Explainable AI to contribute to fundamental R&D on understanding what modern AI models learn and how that knowledge is represented internally. As ...

Dark Wolf Solutions

AI Software Engineer

Chantilly, VA · Remote

$120K - $160K/yr

Implement robust Explainable AI (XAI) techniques to ensure transparency and trustworthiness of model recommendations. * Ensure all AI models, APIs, and data storage solutions strictly adhere to DoD ...

Dark Wolf Solutions

AI Software Engineer

Herndon, VA · On-site

$120K - $160K/yr

Implement robust Explainable AI (XAI) techniques to ensure transparency and trustworthiness of model recommendations. * Ensure all AI models, APIs, and data storage solutions strictly adhere to DoD ...

Dark Wolf Solutions

AI Software Engineer

Chantilly, VA · Remote

$120K - $160K/yr

Implement robust Explainable AI (XAI) techniques to ensure transparency and trustworthiness of model recommendations. * Ensure all AI models, APIs, and data storage solutions strictly adhere to DoD ...

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Explainable Ai information

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$112K

$156.5K

How much do explainable ai jobs pay per year?

As of Sep 1, 2026, the average yearly pay for explainable ai in the United States is $112,009.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,500.00 and $127,000.00 per year, depending on experience, location, and employer.

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 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 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 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.

More about Explainable Ai jobs

What cities are hiring for Explainable Ai jobs?

Cities with the most Explainable Ai job openings:

What states have the most Explainable Ai jobs?

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What job categories do people searching Explainable Ai jobs look for?

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Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $112,009 per year, or $53.9 per hour.

Explainable AI Engineer

Brain Gain Recruiting

Palo Alto, CA • Remote

$122K - $165K/yr

Full-time, Part-time

Posted 15 days ago

Job description

Company Description

We’re an Industrial AI start-up founded by a Stanford professor and led by recognized leaders in Data Intelligence for Aerospace & Defense supply chain operations. Our Enterprise SaaS AI Application improves sustainment of the U.S. Air Force’s 5,000-aircraft fleet ($100B yearly budget). Having delivered $200M+ in cost avoidances, we are growing 4x in repeat contracts.

We have built and are operationally deploying hallucination-free Agentic AI for mission critical decision support that relies on proprietary Explainable AI engines for SME-explainable insights from existing data.

Job Description

We’re hiring an Explainable AI Engineer to build, verify, and validate business impact predictions based on outputs of our AI Engines. You’ll have an opportunity to learn from Stanford and Georgia Tech professors and work across algorithms and data pipelines to evolve our Explainable AI including

  • Development of Operations Research features for the AI models
  • Automation of data management towards scalability
  • Aerospace grade V&V for mission critical functions using simulations
  • Explainable AI engine interfaces to data, human users, and Agentic AI

Main Responsibilities:

  • Understand, develop, & support business impact prediction analytics in Explainable AI Engines
  • Build, evolve, verify, and validate data processing pipeline segments
  • Support scalable deployment of the AI
  • Integrate Explainable AI with LLM tools for Agentic AI experience
  • Collaborate with the cloud software team and occasionally with customers
  • Document Explainable AI for customer users and internal software developers
Qualifications

Must-haves

  • Experience building mission-critical analytical applications, working in a production environment
  • Hands-on skills in at least some of mathematical methods in:
    • Operations Research
    • Bayesian statistics
    • Monte Carlo analysis
    • Mathematical optimization
    • Decision and Control
  • Experience with MATLAB
  • Model-based development / simulation-based verification
  • Solid experience writing technical documentation
  • Strong collaboration and communication skills; experience working with a software team
  • Graduate Degree in Engineering or a related field
  • U.S. Citizenship.

Pluses

  • Supply chain and/ or sustainment in the aerospace industry
  • Experience with Java and GitHub (Actions)
  • Building SaaS applications
  • Experience with GenAI and LLM augmentation (RAG)
  • PhD

Additional Information
  • US Citizenship Required: This position requires the ability to work with Controlled Unclassified Information for the Department of Defense.
  • A background check and a drug check may be required.
  • Location: This is a remote position. USA, Nationwide.
  • You will have an opportunity to start as a contractor (preferred; full-time or part-time, at least 10 hours/ week), or a full-time employee.

Apply