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Ai Qa Jobs (NOW HIRING)

Finite State

In our environment, AI is a first-class partner in software creation. The QA Staff/Lead Engineering Manager will define how quality is engineered into the product using automation, AI agents ...

Meta

QA Engineering Lead, AI Native

Menlo Park, CA

$138K/yr

QA Engineering Lead, AI Native Responsibilities: * Build and foster a quality-driven engineering environment that enables rapid, confident product releases, ensuring that quality is embedded ...

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How much do ai qa jobs pay per hour?

As of May 28, 2026, the average hourly pay for ai qa in the United States is $40.13, according to ZipRecruiter salary data. Most workers in this role earn between $28.12 and $49.28 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI QA (Artificial Intelligence Quality Assurance) specialist, and why are they important?

To thrive as an AI QA specialist, you need strong analytical skills, knowledge of software testing methodologies, and a solid understanding of AI/ML concepts, typically supported by a degree in computer science or a related field. Familiarity with testing frameworks like Selenium or PyTest, version control systems like Git, and tools such as TensorFlow or PyTorch for model validation is often required. Attention to detail, problem-solving abilities, and effective communication set top performers apart in this role. These skills ensure the reliability, fairness, and safety of AI systems, which is critical for delivering trustworthy and high-quality AI solutions.

How does an AI QA engineer typically collaborate with data scientists and developers during the model development lifecycle?

AI QA engineers work closely with data scientists and developers throughout the AI model development process. They often participate in sprint planning and daily stand-up meetings to align on testing strategies, clarify requirements, and address potential model issues early. Their responsibilities include designing test cases for model accuracy, fairness, and robustness, as well as validating data pipelines and deployment workflows. Effective communication and a proactive approach are essential, as AI QA engineers help ensure the final model meets quality standards and integrates smoothly into production environments.

What are AI QA professionals?

AI QA (Artificial Intelligence Quality Assurance) professionals are specialists who test and validate machine learning models, AI systems, and related software to ensure they function correctly, reliably, and ethically. They design test cases, evaluate model accuracy, check for biases, and verify compliance with regulatory standards. AI QA experts work closely with data scientists, developers, and product managers to identify issues and improve system performance throughout the AI development lifecycle.

What is the difference between Ai Qa vs Data Analyst?

AspectAi QaData Analyst
Required CredentialsTypically certifications in AI, QA, or software testingBachelor's degree in data science, statistics, or related fields
Work EnvironmentTech companies, software development teams, AI-focused projectsBusiness, finance, healthcare, and various industries analyzing data
Employer & Industry UsageTech firms, AI startups, software companiesCorporations across multiple sectors, consulting firms
Common Search & Comparison IntentUnderstanding roles in AI quality assuranceAnalyzing data to inform business decisions

Ai Qa specialists focus on testing and ensuring the quality of AI systems, often requiring knowledge of AI tools and testing protocols. Data Analysts interpret data to generate insights, requiring skills in data visualization and statistical analysis. While both roles work with data and technology, Ai Qa emphasizes quality assurance in AI development, whereas Data Analysts focus on data interpretation for decision-making.

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Ai Qa jobs near you
Infographic showing various Ai Qa job openings in the United States as of May 2026, with employment types broken down into 72% Full Time, 14% Part Time, and 14% Contract. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $83,472 per year, or $40.1 per hour.

AI & QA Consultant - Procedure Documentation

Leading Utilities Organization

Chandler, AZ • On-site, Remote

$82 - $85/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

AI & QA Consultant – Procedure Documentation

Location (Hybrid – 3 days onsite / 2 remote): Charlotte, NC, Chandler, AZ, Minneapolis, MN

Duration: 12-Month Contract (W2 ONLY) Strong potential for extension or conversion (USC or GC Holder Only) Compensation: $82–$85/hour

Overview: We are seeking an experienced consultant to lead quality assurance and AI enablement initiatives for enterprise procedure documentation. This role will focus on improving the quality, usability, and consistency of thousands of documented procedures while helping scale AI-driven solutions to support content creation and evaluation.

The ideal candidate will bring strong experience in technical/procedure writing, content evaluation, and practical use of AI tools (such as Copilot) to improve documentation processes.

Key Responsibilities:

  • Establish and evolve a quality assurance (QA) framework to evaluate procedure documentation across ~8,000+ artifacts
  • Review published procedures and assess content based on usability, readability, and completeness
  • Define and apply clear evaluation criteria (e.g., satisfactory, watch, at-risk) to prioritize content improvements
  • Identify gaps in documentation that may create operational or compliance risks
  • Partner with business teams and documentation authors to improve content quality and consistency
  • Work closely with technology teams to define rules of writing that can be translated into AI-driven solutions
  • Help design and refine AI prompts used for generating and evaluating procedures
  • Act as a subject matter advisor on how AI can be used to improve documentation quality and efficiency
  • Promote adoption of AI tools (e.g., Copilot, Power Platform) across a broad base of non-technical "citizen writers"
  • Support development of a centralized knowledge base for writing standards, guidance, and reusable AI prompts
  • Provide editorial guidance without directly authoring content; focus on review, standards, and quality enforcement
  • Influence and guide teams through the transition to AI-assisted documentation practices

Required Qualifications:

  • 5+ years of experience in technical writing, procedure writing, or documentation quality review
  • Strong understanding of writing standards (clarity, structure, active voice, consistency, completeness)
  • Experience reviewing and improving procedural or operational documentation
  • Practical experience using AI tools (e.g., Copilot or similar) for writing, editing, or analysis
  • Ability to define rules and standards that can be applied consistently across large volumes of content
  • Strong communication skills and ability to work with both business SMEs and technical teams

Preferred Qualifications:

  • Experience building or working within a documentation QA or editorial framework
  • Familiarity with AI prompt design or working with teams that implement AI-based solutions
  • Experience with tools such as Microsoft Copilot, Power Platform (Power Automate, Power Apps), or similar
  • Background supporting large-scale content environments with multiple authors
  • Experience working in regulated or process-heavy environments

Role Characteristics:

  • Acts as the quality gate for published documentation (editorial QA role, not a primary writer)
  • Works with a large, distributed group of contributors ("citizen writers")
  • Balances process design, content evaluation, and AI enablement
  • Plays a key role in shaping how AI is used across the organization for documentation

What Success Looks Like:

  • Clear, consistent QA standards applied across thousands of documents
  • Improved readability and usability of procedures
  • Scalable AI-assisted documentation practices adopted across teams
  • Reduced risk from unclear or incomplete process documentation

Apply