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$71.5K

$112K

$156.5K

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.

Product & Technical Co-founder (CPTO) - AI Compliance Platform

FutureSight

Seattle, WA

$190K - $219K/yr

Other

Posted 18 days ago

Job description

The Opportunity

Compliance at U.S. RIAs, broker-dealers, and wealth fintechs still runs largely on manual review - analysts reading marketing materials line by line, scanning communications for keywords, and reviewing trades and trade requests by hand. Across more than 25,000 firms, operational costs are significant, and regulatory exposure is even greater.

Global RegTech spend is projected to grow from roughly $12B to more than $66B over the next decade, with the US growing to $32B. The teams that ship credible, audit-ready platforms in the next 18 months will define the category. We intend to be one of them.

The Venture

The answer is not another rules engine. We are building an explainable AI platform that reasons like a senior compliance officer, cites firm policy and SEC/FINRA regulations for every decision, and produces audit-ready evidence on demand - across marketing, communications, trading, policy, audit, and filings.

The platform is designed to operate alongside the systems compliance teams already use, replacing manual review with explainable AI judgments that hold up to regulator scrutiny. The roadmap moves from marketing review into communications surveillance, policy and manuals, audit, and ultimately regulatory filings and enterprise integrations.

The Partnership

The CEO co-founder is in place. The customer thesis is validated, design partners are engaged, and the market work is complete. What remains is to bring on the technical co-founder - a partner who will own product and engineering with the same conviction, urgency, and ownership stake.

This is a co-founder role, not a CPTO hire. In return for that level of commitment, you receive founder-level equity, founder-level authority with co-decision rights on product, technology, hiring, fundraising, and strategy, a board or board-observer seat as appropriate to the cap table and stage, and a genuine partnership with the CEO on every material decision.

What you'll own

As Co-Founder & CPTO, you will write the first lines of production code, set the technical direction, and lead execution end-to-end.

  • Architecture & Engineering Culture - Establish the core architecture, engineering culture, and security posture, including SOC 2 readiness, tenant isolation, encryption, and audit logging
  • Product Build - Ship a marketing review prototype into production with a design partner early adopter, then deliver communications surveillance, trading oversight, audit-ready logs and reporting; release GA v1 covering all core modules except regulatory filings within 9 months, and expand into regulatory filings and enterprise integrations through 18 months
  • AI/LLM Build - Own the training and constant evolution of the core AI/LLM model, delivering robust and highly reliable analysis
  • Explainability & Reliability - Own the explainability layer that produces audit-ready evidence on demand, and the reliability engineering that ensures the platform performs consistently in the field
  • Customer & Market - Represent the company in front of all customer constituencies and third-party interests, as well as investors, as the technical voice of the venture
  • Capital - Convert pilots into ten paying customers and reach $1M ARR within 18 months, partnering with the CEO on conversion and fundraising
  • Team - Recruit and lead the founding engineering and applied AI team, and establish the cultural foundation of the company
Co-Founder Profile
  • Shipped LLM-powered systems into production environments where incorrect outputs carried real consequences - not prototypes or demonstrations
  • Full-stack confidence across applied LLMs, RAG, retrieval, deterministic rules engines, human-in-the-loop workflows, observability, and secure multi-tenant SaaS (SOC 2, tenant isolation, encryption, audit logging)
  • Well-formed convictions about explainability and reliability engineering, developed through direct production experience
  • Preference for systems that perform consistently in the field over systems that demonstrate well in controlled settings
  • Prepared to operate as a principal - setting the technical agenda, making decisive calls under uncertainty, and owning the outcomes that follow
  • Strongly preferred: prior founder, founding engineer, or early-stage technical leadership experience; background in legal tech, RegTech, fintech compliance, healthcare, or another high-trust regulated vertical; familiarity with the U.S. wealth management ecosystem, including SEC and FINRA workflows, marketing review, communications surveillance, and trade monitoring; direct experience self-hosting or privately deploying foundation models
How to Apply

Please submit your resume, LinkedIn profile, and a brief note on why this venture aligns with your goals as a founder. We will move quickly for the right candidate.

FutureSight is committed to diversity, equity, and inclusion. We welcome applicants of all backgrounds and experiences.

Apply