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Ai Test Engineer Jobs in Minnesota (NOW HIRING)

Infosys Limited Digital

Test Engineer

Saint Paul, MN · On-site

We focus on adopting a data-first approach to engineering and value-stream management, enabling ... Our team leverages advanced technologies such as AI, ML, and predictive analytics to drive ...

CommScope Inc.

Product Test Engineer

Shakopee, MN · On-site

$76K - $95K/yr

Together, we help operators stay ahead of evolving AI applications and increasing demands. General ... Collaboration with current lab staff which includes other test engineers and technicians in team ...

CommScope, Inc.

Product Test Engineer

Shakopee, MN · On-site

$76K - $95K/yr

Together, we help operators stay ahead of evolving AI applications and increasing demands. General ... Collaboration with current lab staff which includes other test engineers and technicians in team ...

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All JobsAi Test Engineer JobsAi Test Engineer Jobs in Minnesota

Ai Test Engineer information

See Minnesota salary details

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$43

$73

How much do ai test engineer jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for ai test engineer in Minnesota is $43.33, according to ZipRecruiter salary data. Most workers in this role earn between $32.74 and $51.35 per hour, depending on experience, location, and employer.

What is the difference between Ai Test Engineer vs Data Scientist?

AspectAi Test EngineerData Scientist
Required CredentialsBachelor's in CS, AI, or related field; knowledge of testing toolsBachelor's or higher in CS, Statistics, or related; proficiency in data analysis
Work EnvironmentSoftware testing teams, AI development projectsData analysis teams, AI research projects
Employer & Industry UsageTech companies, AI startups, software firmsTech companies, finance, healthcare, research institutions
Common Search & ComparisonOften compared for roles in AI testing and quality assuranceCompared for data analysis and AI model development roles

While both roles work within AI and tech environments, Ai Test Engineers focus on testing and validating AI systems, ensuring quality and performance. Data Scientists analyze data to develop models and insights. The roles are complementary but distinct in their core responsibilities.

What is an AI test engineer?

AI Test Engineers are professionals who design, develop, and execute tests to evaluate the performance, accuracy, and reliability of artificial intelligence systems and machine learning models. They work closely with data scientists and software developers to ensure AI solutions function as intended, identifying bugs, biases, and potential risks in algorithms. Their responsibilities often include creating test cases, automating test processes, and analyzing results to improve AI system quality and compliance.

What are some common challenges AI test engineers face when validating machine learning models?

AI Test Engineers often encounter challenges such as ensuring the quality and fairness of machine learning models, identifying edge cases that the model may not handle well, and working with limited labeled data for testing. Additionally, interpreting test results can be complex due to the probabilistic nature of AI outputs, requiring close collaboration with data scientists to understand model behaviors. Effective communication and a strong foundation in both software testing and AI concepts are essential to address these challenges and ensure reliable AI solutions.

What are the key skills and qualifications needed to thrive as an AI test engineer?

To thrive as an AI Test Engineer, you need a strong background in computer science, software testing methodologies, and a good understanding of machine learning concepts, often supported by a relevant degree. Familiarity with programming languages like Python, testing frameworks such as pytest, and tools like TensorFlow or PyTorch, along with certifications in software testing or AI, are typically required. Analytical thinking, attention to detail, and effective communication set top performers apart in this role. These skills and qualities are crucial for ensuring the reliability, accuracy, and ethical deployment of AI systems.
What are popular job titles related to Ai Test Engineer jobs in Minnesota? For Ai Test Engineer jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Ai Test Engineer jobs in Minnesota look for? The top searched job categories for Ai Test Engineer jobs in Minnesota are:
What cities in Minnesota are hiring for Ai Test Engineer jobs? Cities in Minnesota with the most Ai Test Engineer job openings:
Ai Test Engineer jobs near you
Infographic showing various Ai Test Engineer job openings in Minnesota as of August 2026, with employment types broken down into 10% Internship, 70% Full Time, and 20% Contract. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $90,132 per year, or $43.3 per hour.

AVP, AI Quality & Reliability Engineering

Vizient, Inc.

Edina, MN • On-site

Full-time

Re-posted 8 days ago

Job description

When you're the best, we're the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.
In this role you will lead the strategy, operationalization, governance alignment, and continuous evolution of enterprise AI Quality Engineering capabilities across Vizient. You will establish scalable AI Quality Engineering operating models, validation frameworks, runtime quality practices, synthetic data ecosystems, simulation-driven testing capabilities, and enterprise quality standards supporting responsible industrialization of AI-powered business solutions at enterprise scale and enterprise AI test data modernization, synthetic data platforms, healthcare digital simulation ecosystems, and hospital twin capabilities supporting secure, realistic, and scalable AI validation. You will Partner closely with AI Engineering & Delivery, AI Operations, Governance, Security, Clinical, Data, and Business teams to ensure AI solutions are secure, reliable, observable, compliant, scalable, and aligned with enterprise quality and healthcare operational expectations. In this role you will combine enterprise quality engineering leadership, AI-enabled testing modernization, healthcare workflow validation, governance alignment, and cross-functional organizational leadership.
AI Quality Engineering Leadership
  • Lead enterprise AI Quality Engineering initiatives across Vizient, including AI-powered applications, LLM-enabled workflows, intelligent automation solutions, agentic systems, and enterprise AI platforms.
  • Establish and mature AI Quality Engineering capabilities, including AI-native testing strategies, validation frameworks, runtime assurance practices, scalable operating models, and reusable quality accelerators.
  • Modernize traditional Quality Engineering practices to support AI-enabled workflows, probabilistic systems, intelligent orchestration, and evolving healthcare operational workflows.
  • Define enterprise AI quality standards, testing methodologies, validation approaches, release readiness criteria, and governance-aligned quality practices supporting scalable AI adoption across the enterprise.
  • Provide executive oversight across AI validation, quality engineering, test automation, runtime quality monitoring, release readiness, and quality improvement initiatives.
  • Lead organizational transformation efforts supporting the evolution of traditional QA capabilities toward AI-native quality engineering and simulation-driven validation practices.

AI Validation, Testing & Runtime Assurance
  • Lead enterprise AI validation strategies, including functional validation, prompt testing, workflow testing, regression testing, runtime quality assurance, and production reliability practices.
  • Partner with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, and Data teams to establish scalable quality engineering processes supporting enterprise AI development lifecycle management and production operationalization.
  • Support enterprise AI runtime quality practices, including telemetry integration, monitoring alignment, incident coordination, release validation, deployment readiness assessments, and runtime reliability improvement initiatives.
  • Drive modernization of enterprise test automation capabilities leveraging AI-assisted testing, intelligent automation, reusable testing accelerators, scalable quality engineering frameworks, and measurable quality metrics.
  • Support enterprise AI observability and evaluation initiatives to improve reliability, traceability, runtime visibility, and operational confidence across AI-enabled systems.
  • Collaborate with Clinical, Operational, and Engineering stakeholders to support validation approaches for healthcare workflows, payer operations, and AI-enabled business processes.

AI Test Data, Synthetic Data & Simulation Platforms
  • Lead enterprise strategies supporting AI test data modernization, synthetic data capabilities, simulation environments, and scalable testing ecosystems enabling secure, realistic, and enterprise-grade AI validation.
  • Oversee development of enterprise synthetic test data platforms, healthcare simulation ecosystems, and hospital twin environments supporting AI engineering, testing, validation, and deployment readiness.
  • Partner with Data, Clinical, Engineering, Security, Governance, and AI teams to support scalable simulation frameworks, realistic operational testing scenarios, PHI-safe validation environments, and AI-enabled testing acceleration initiatives.
  • Support development of simulated healthcare operational environments and hospital twin capabilities enabling workflow validation, operational stress testing, scenario simulation, and safe evaluation of AI-enabled healthcare processes.
  • Drive scalable approaches for synthetic data governance, simulation fidelity, test environment automation, and secure testing ecosystem management within regulated healthcare environments.

Governance, Risk & Responsible AI
  • Partner with AI Governance, Risk, Compliance, Security, Clinical, and Operational stakeholders to support responsible AI practices, operational safeguards, human oversight controls, validation traceability, auditability, and secure deployment of AI solutions.
  • Support development and implementation of enterprise AI quality controls, validation evidence processes, testing governance frameworks, risk mitigation practices, and quality review standards aligned with enterprise and regulatory expectations.
  • Help establish scalable processes supporting secure, compliant, reliable, and measurable AI solution deployment within regulated healthcare environments.

Portfolio, Delivery & Organizational Scaling
  • Lead AI Quality Engineering portfolio activities, including organizational planning, vendor coordination, contractor oversight, delivery prioritization, execution governance, and scalable capability development.
  • Partner with enterprise stakeholders to evaluate implementation readiness, quality risks, scalability considerations, testing feasibility, and delivery dependencies for prioritized AI initiatives.
  • Support tooling evaluations, platform assessments, automation strategies, and modernization initiatives supporting enterprise AI Quality Engineering maturity.
  • Define scalable squad structures, delivery engagement models, support processes, and governance approaches supporting enterprise AI transformation initiatives.
  • Help drive organizational capability development, workforce maturation, operating model evolution, and enterprise adoption of modern AI Quality Engineering practices.

Leadership, Communication & Culture
  • Lead, mentor, and develop quality engineering leaders, managers, engineers, analysts, and contractor teams while fostering a collaborative, continuously learning, and engineering-driven culture.
  • Communicate quality risks, testing strategies, governance implications, implementation tradeoffs, and strategic recommendations effectively to both technical and executive stakeholders.
  • Promote a culture of engineering excellence, continuous improvement, enterprise accountability, responsible AI adoption, and operational discipline.
  • Research and evaluate emerging AI quality engineering, testing, observability, validation, simulation, automation, and runtime assurance technologies supporting innovation and continuous improvement initiatives.

Estimated Hiring Range:
At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $156,500.00 to $290,100.00.
This position is also incentive eligible.
Vizient has a comprehensive benefits plan! Please view our benefits here:
http://www.vizientinc.com/about-us/careers
Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities
The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.

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