ZipRecruiter

Log In

1

Explainable Ai Xai Jobs (NOW HIRING)

System One

Be Seen First

Data Scientist (AI)

Washington, DC · Remote

$125K - $190K/yr

Experience developing explainable AI (XAI) solutions, including confidence scoring and traceability of results. * Experience designing analytics dashboards or reporting solutions for end users.

Commence

Technical Lead

Leesburg, VA · On-site

Experience with explainable AI (xAI) outputs in federal audit or compliance workflows * CISSP, CISM, or equivalent security certifications preferred

Guidehouse

AI/ML Data Scientist

Arlington, VA · On-site

$113K - $188K/yr

Experience developing explainable AI (XAI) solutions, including confidence scoring and traceability of results. * Experience designing analytics dashboards or reporting solutions for end users.

Guidehouse

AI/ML Data Scientist

Arlington, VA

Experience developing explainable AI (XAI) solutions, including confidence scoring and traceability of results. * Experience designing analytics dashboards or reporting solutions for end users.

Guidehouse

AI/ML Data Scientist

Arlington, VA

$113K - $188K/yr

Experience developing explainable AI (XAI) solutions, including confidence scoring and traceability of results. * Experience designing analytics dashboards or reporting solutions for end users.

Software People, Inc.

UI/UX Architect

Austin, TX · On-site

Demonstrated experience designing user interfaces for systems that incorporate Artificial Intelligence or Machine Learning, with a focus on explainable AI (XAI) and human-AI interaction principles ...

next page

Showing results 1-20

All JobsExplainable Ai Xai Jobs

Explainable Ai Xai information

See salary details

$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.

Research Engineer, AI Safety & Alignment

Character.ai

Redwood City, CA • On-site

$225K - $400K/yr

Full-time

Posted 28 days ago

Key responsibilities

  • Develop and implement evaluation methodologies and metrics to assess the safety and alignment of large language models.

  • Research and develop techniques for model alignment, value learning, and interpretability.

  • Conduct adversarial testing to uncover potential vulnerabilities and failure modes in models.

Job description

About the role and team
Joining us as a Research Engineer, you'll be at the forefront of tackling one of the most critical challenges in AI today: safety and alignment. Your work will be pivotal in understanding and mitigating the risks of advanced AI, conducting foundational research to make our models safer, and solving the core technical problems of AI alignment-ensuring our models behave in accordance with human values and intentions.
The Safety team is dedicated to pioneering and implementing techniques that make our models more robust, honest, and harmless. As a Research Engineer, you will bridge the gap between theoretical research and practical application, writing high-quality code to test hypotheses and integrating successful safety solutions directly into our products. Your research will not only protect millions of users but also contribute to the broader scientific community's understanding of how to build safe, beneficial AI.
What you'll do
  • Develop and implement novel evaluation methodologies and metrics to assess the safety and alignment of large language models.
  • Research and develop cutting-edge techniques for model alignment, value learning, and interpretability.
  • Conduct adversarial testing to proactively uncover potential vulnerabilities and failure modes in our models.
  • Analyze and mitigate biases, toxicity, and other harmful behaviors in large language models through techniques like reinforcement learning from human feedback (RLHF) and fine-tuning.
  • Collaborate with engineering and product teams to translate safety research into practical, scalable solutions and best practices.
  • Stay abreast of the latest advancements in AI safety research and contribute to the academic community through publications and presentations.
Who you are
  • Hold a PhD (or equivalent experience) in a relevant field such as Computer Science, Machine Learning, or a related discipline.
  • Write clear and clean production-facing and training code
  • Experience working with GPUs (training, serving, debugging)
  • Experience with data pipelines and data infrastructure
  • Strong understanding of modern machine learning techniques, particularly transformers and reinforcement learning, with a focus on their safety implications.
  • Are passionate about the responsible development of AI and dedicated to solving complex safety challenges.
Nice to Have
  • Experience with product experimentation and A/B testing
  • Experience training large models in a distributed setting
  • Familiarity with ML deployment and orchestration (Kubernetes, Docker, cloud)
  • Experience with explainable AI (XAI) and interpretability techniques.
  • Have research in AI safety, alignment, ethics, or a related area.
  • Knowledge of the broader societal and ethical implications of AI, including policy and governance.
  • Publications in relevant academic journals or conferences in the field of machine learning

About Character.AI
Character.AI empowers people to connect, learn and tell stories through interactive entertainment. Over 20 million people visit Character.AI every month, using our technology to supercharge their creativity and imagination. Our platform lets users engage with tens of millions of characters, enjoy unlimited conversations, and embark on infinite adventures.
In just two years, we achieved unicorn status and were honored as Google Play's AI App of the Year-a testament to our innovative technology and visionary approach.
Join us and be a part of establishing this new entertainment paradigm while shaping the future of Consumer AI!
At Character, we value diversity and welcome applicants from all backgrounds. As an equal opportunity employer, we firmly uphold a non-discrimination policy based on race, religion, national origin, gender, sexual orientation, age, veteran status, or disability. Your unique perspectives are vital to our success.

Apply