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Explainable Ai Jobs (NOW HIRING)

Brain Gain Recruiting

Explainable AI Engineer

Palo Alto, CA · On-site +1

$114K - $157K/yr

We're hiring an Explainable AI Engineer to build, integrate, and deploy our AI Engines and Agentic AI workflows. You'll have an opportunity to learn from Stanford and Georgia Tech professors and work ...

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 ...

Accenture Federal Services

Data Scientist

Arlington, VA · On-site

Design, build, and deploy explainable AI models using Python AI ecosystems, includingTorch, Scikit-learn, and LLM frameworks * Collaborate with cross-functional teams to integrate AI, DevSecOps, data ...

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Explainable Ai information

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How much do explainable ai jobs pay per year?

As of Jul 2, 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 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 is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior AI researcher, machine learning director, or AI solutions architect, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership responsibilities, strategic planning, and expertise in tools like Python, TensorFlow, or PyTorch, and may require relevant certifications or advanced degrees. Compensation at this level reflects significant experience and impact within the organization.

What degree is needed for XAI jobs?

Explainable AI (XAI) jobs typically require a bachelor's degree in computer science, data science, or a related field, with many roles preferring or requiring a master's or Ph.D. in artificial intelligence, machine learning, or a similar discipline. Strong programming skills, knowledge of machine learning frameworks, and understanding of model interpretability are also important for these roles.

What is the highest paying AI job?

The highest paying AI jobs typically include roles such as AI research director, machine learning engineer, and AI solutions architect, often requiring advanced degrees and expertise in deep learning, natural language processing, or computer vision. These positions can offer salaries exceeding $150,000 annually, especially in tech hubs or large organizations with specialized AI needs.

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

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.

Which 3 jobs will survive AI?

Explainable AI specialists, data scientists, and AI ethics professionals are likely to continue thriving as AI advances, because their roles involve understanding, interpreting, and ensuring transparency of AI systems. These jobs require critical thinking, domain expertise, and communication skills that are difficult to automate fully. Continuous learning and familiarity with AI tools and frameworks are essential for these roles to remain relevant.
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? States with the most job openings for Explainable Ai jobs include:
What job categories do people searching Explainable Ai jobs look for? The top searched job categories for Explainable Ai jobs are:
Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in the United States as of June 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $112,009 per year, or $53.9 per hour.
Explainable AI Engineer

Explainable AI Engineer

Brain Gain Recruiting

Palo Alto, CA • On-site, Remote

$114K - $157K/yr

Full-time, Part-time

Posted 13 hours 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 are building hallucination-free Agentic AI for mission critical decision support where GenAI relies on proprietary Explainable AI engines for SME-explainable insights from historical data.

Job Description

We’re hiring an Explainable AI Engineer to build, integrate, and deploy our AI Engines and Agentic AI workflows. 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 deep math features for the models, ML, and inferences
  • Automation of data wrangling and cleansing 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 deep math 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 with productized analytical applications
  • Experience building mission-critical applications as a part of a team
  • Hands-on skills in at least some of mathematical methods in:
    • Statistics
    • Bayesian estimation
    • Mathematical optimization
    • Decision and Control
  • Experience with MATLAB
  • Experience in Java and GitHub (Actions).
  • Solid experience writing technical documentation
  • Strong collaboration and communication skills.
  • Bachelor’s Degree in Engineering or a related field
  • U.S. Citizenship.

Pluses

  • Model-based development / simulation-based verification
  • Operations Research
  • Supply chain and/ or MRO in the aerospace industry
  • Building SaaS type applications
  • Shell scripting, Golang.
  • Experience with GenAI and LLM augmentation (RAG)
  • An advanced degree

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. Proximity to either our Palo Alto, CA or Atlanta, GA office may be a plus.
  • 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.