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Explainable Ai 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 ...

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 ...

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 ...

MANTECH

Senior AI Policy Analyst

Columbia, MD · On-site

Conduct thorough research and analysis of emerging AI technologies, including Generative AI, Quantum AI, and Explainable AI, and their policy implications. * Collaborate with government leads and AI ...

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How much do explainable ai jobs pay per year?

As of Jul 23, 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 July 2026, with employment types broken down into 73% Full Time, 24% Part Time, and 3% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution, with an average salary of $112,009 per year, or $53.9 per hour.

AI Engineer / Research Scientist (Senior, Staff), Explainable AI

Seekr

Austin, TX • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 25 days ago

Job description

Seekr's Mission:
Seekr builds trusted AI for mission-critical decisions. Our platform helps organizations build, govern, and deploy secure, explainable AI rooted in their own data across cloud, on-premises, edge, and air-gapped environments. We care deeply about transparency, auditability, and defensibility because high-stakes AI is only useful when people can understand and trust how it behaves.
About the Opportunity:
The first wave of AI was about scale. The frontier now is reliable AI: systems that are not only capable, but understandable, testable, and dependable in real decisions. At Seekr, explainability is not a reporting layer added after deployment; it is a core product and research problem spanning attribution and interpretability, observability, and contestability. This role sits directly in that high-impact space, helping turn state-of-the-art ideas into production capabilities customers can trust.
We are open to candidates from either research scientist or engineering backgrounds. Success in this role requires strength in one domain, and working proficiency in the other.
What You'll Do:
  • Design and build explainability capabilities that help users understand why a model or agent produced a given output and what training data, retrieved documents, tools, agent interactions, or internal model mechanisms influenced that result.
  • Design and build contestability capabilities that enable users to challenge AI outputs, capture corrective feedback, and turn contested results into data that improves systems over time.
  • Work on adjacent high-impact areas such as hallucination detection and mitigation, and continual-learning agents that can learn from explainability signals and contested outputs.
  • Translate and synthesize promising ideas from current literature into prototypes, and translate validated prototypes into production-grade features.
  • Contribute across the AI system lifecycle where needed, including model development, inference, deployment, and monitoring.
  • Partner with product, design, and customer-facing teams to make explainability useful in real workflows, not just technically interesting.
  • Use AI coding assistants effectively and reliably as part of a modern engineering workflow while maintaining strong judgment and code quality.

What We're Looking For:
  • Strong background in machine learning and modern AI systems, including LLM/VLMs, agent frameworks, RAG, or adjacent applied ML systems.
  • Ability to move comfortably between research and engineering.
  • Scientists here should be able to write production-grade code when needed; engineers here should be able to prototype and pressure-test systems inspired by state-of-the-art papers.
  • Experience designing experiments and evaluating ambiguous technical tradeoffs.
  • Fluency with AI coding assistants and the modern developer workflows they enable.
  • Strong Python and software engineering fundamentals, with comfort in testing, code review, CI/CD, debugging, and performance analysis.
  • Clear communication and strong collaboration across technical and non-technical partners.
  • Reside near Austin, TX or Reston, VA and able to work 3 days per week in office

Preferred Qualifications, Research Scientist-Leaning Candidates:
  • Master's or PhD in computer science, machine learning, AI, statistics, or a related field preferred.
  • Experience in explainable and interpretable AI, such as feature attribution methods like LIME and SHAP, example- or influence-based attribution, or mechanistic interpretability.
  • Track record of original technical work, such as publications, patents, open-source contributions, or research that materially shaped shipped systems.

Preferred Qualifications, Engineer-Leaning Candidates:
  • Experience designing end-to-end AI systems from data preparation and evaluation through serving, deployment, monitoring, and iteration.
  • Experience with inference and serving stacks such as vLLM, SGLang, or similar systems.
  • Experience optimizing model serving for latency, throughput, batching, caching, memory efficiency, quantization, and cost/performance tradeoffs.
  • Experience with API/SDK development and building usable platform abstractions for other developers.
  • Experience with Kubernetes-based deployment, CI/CD, and GitOps workflows such as Argo CD.
  • Experience working with GPU/accelerator environments, containerized ML workloads, and production performance tuning; experience with custom GPU/accelerator kernels for AI workload optimization is a plus.
  • Experience with database and retrieval system design, including relational stores, vector databases, and RAG architectures.
  • Experience with experiment tracking, model/data versioning, evaluation pipelines, observability, and diagnosing production issues in AI systems.

Nice to Have:
  • Experience designing AI systems with human oversight, review, approval, or override workflows.
  • Familiarity with governance, provenance, security, and auditability requirements for enterprise or government AI.
  • Experience deploying AI across cloud, on-prem, edge, or air-gapped environments.

Why This Role Matters:
Explainability is becoming infrastructure, not a side feature. As AI moves deeper into operational, regulated, and business-critical workflows, the systems that win will be the ones people can interrogate, validate, and defend. This role is a chance to help define that standard at a company built around trusted AI from the start.
About the Company:
Seekr is a leader in explainable and trustworthy artificial intelligence designed to power mission-critical decisions in enterprises, government, and regulated industries. SeekrFlow™, our end-to-end AI platform, provides secure, auditable AI solutions tailored to sectors where transparency, accuracy, and compliance are paramount. Available across cloud, on-premises, and edge environments, SeekrFlow reduces bias, strengthens data integrity, and simplifies model oversight so organizations can rely on trusted AI decisions in high-stakes settings that impact society's most sensitive and vital systems. Trusted by leading enterprises and government agencies, we partner with defense, finance, telecom, and critical infrastructure leaders to enable AI solutions that drive real-world results with unmatched transparency and control. We are a team of strategic thinkers and problem-solvers tackling the toughest challenges facing critical infrastructure and global enterprises through best-in-class AI models and customer deployment. Our team operates with unwavering commitment to our core values and mission:
  • We are driven by outcomes-our customers' success is what we strive for every day.
  • We believe trust is earned, which is why we build explainability and transparency into the entire AI lifecycle.
  • We take our responsibility to deliver secure AI seriously.
  • We believe innovation drives progress-we are building the technologies that power the systems our society depends on.

Company Benefits:
  • Meaningful Mission & Impact - Work with a deeply talented, collaborative team solving some of the toughest AI challenges that matter.
  • Equity Ownership - RSUs that let you share directly in Seekr's long-term success and growth.
  • Time Off That Respects Real Life - Unlimited PTO plus 14 paid company holidays to truly recharge.
  • Work Your Way - A flexible hybrid work environment with offices in Reston, VA and Austin, TX.
  • Competitive Total Rewards - A role-appropriate compensation structure that supports long-term growth, including base salary, bonuses, or commission plans depending on role.
  • 401(k) with Company Match - Build your future with a retirement plan that includes employer matching.
  • Comprehensive Health & Wellness - Medical, dental, vision, and life insurance coverage starting day one-for you and your family.
  • Parental Leave - Paid parental leave to support employees as they welcome a new child through birth, adoption, or foster placement.

Apply