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Generative Ai Testing Jobs in Tennessee (NOW HIRING)

Completes and/or coordinates implementation of design and requirements, testing, operational ... Experience designing and deploying Generative AI solutions using LLMs, RAG architectures, vector ...

Completes and/or coordinates implementation of design and requirements, testing, operational ... Experience designing and deploying Generative AI solutions using LLMs, RAG architectures, vector ...

Completes and/or coordinates implementation of design and requirements, testing, operational ... Experience designing and deploying Generative AI solutions using LLMs, RAG architectures, vector ...

$100K - $120K/yr

Practical generative AI experience, including prompt development, testing, output evaluation, guardrails, and adoption. * Experience using Gong or another conversation intelligence platform to ...

New

Implement and assist in the architecture of advanced Generative AI and Retrieval-Augmented ... Implement solutions with scalability and reliability in mind, incorporating appropriate testing ...

Showing results 41-60

Generative Ai Testing information

What is the difference between Generative Ai Testing vs Data Scientist?

AspectGenerative Ai TestingData Scientist
Required CredentialsKnowledge of AI models, testing tools, programming skillsStatistics, programming, data analysis certifications
Work EnvironmentAI development teams, testing labs, tech companiesResearch labs, tech firms, finance, healthcare
Employer & Industry UsageAI product testing, quality assurance in techData analysis, predictive modeling across industries

Generative Ai Testing focuses on evaluating and validating AI-generated content and models, ensuring quality and accuracy. Data Scientists analyze data, build models, and derive insights. While both roles require programming and AI knowledge, Generative Ai Testing emphasizes testing processes, whereas Data Scientists focus on data analysis and model development.

How do I become a Generative AI Testing?

To become a Generative AI Tester, develop skills in machine learning, natural language processing, and programming languages like Python. Gain experience with AI frameworks such as TensorFlow or PyTorch and understand data quality and model evaluation techniques. Certifications in AI or data science can enhance your qualifications and improve job prospects.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development that involves evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, making it a promising career path with increasing demand as AI technologies expand. Professionals in this area can find opportunities in tech companies, research labs, and startups focused on AI innovation.

What are the key skills and qualifications needed to thrive as a generative AI testing specialist, and why are they important?

To thrive as a Generative AI Testing Specialist, you need a robust understanding of machine learning principles, model evaluation techniques, and a background in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and model evaluation frameworks, as well as experience with automated testing platforms, is typically required. Analytical thinking, attention to detail, and strong communication skills help you identify model weaknesses and collaborate effectively with development teams. These skills are crucial to ensure the reliability, safety, and ethical deployment of generative AI solutions.

What are some common challenges faced when testing generative AI models, and how can I prepare to address them in this role?

Testing generative AI models often involves unique challenges such as evaluating the quality and relevance of generated content, detecting bias or inappropriate outputs, and ensuring model consistency across various prompts. You may work closely with data scientists and engineers to create robust evaluation frameworks and develop automated as well as manual testing strategies. Familiarity with prompt engineering, statistical evaluation techniques, and domain-specific knowledge will help you address these challenges effectively. Proactively staying updated on industry best practices and collaborating with cross-functional teams are key to success in this dynamic field.

What is generative AI testing?

Generative AI Testing refers to the process of evaluating and validating AI systems, particularly those that generate content such as text, images, or code. This type of testing focuses on assessing the accuracy, reliability, fairness, and safety of generative models to ensure they function as intended and avoid producing harmful or biased outputs. Testers use various methods, including automated and manual techniques, to check for issues like hallucinations, inappropriate content, or security vulnerabilities. The goal is to build trust in generative AI systems and ensure they meet quality and ethical standards before deployment.

What are popular job titles related to Generative Ai Testing jobs in Tennessee?

For Generative Ai Testing jobs in Tennessee, the most frequently searched job titles are:

What job categories do people searching Generative Ai Testing jobs in Tennessee look for?

The top searched job categories for Generative Ai Testing jobs in Tennessee are:

What cities in Tennessee are hiring for Generative Ai Testing jobs?

Cities in Tennessee with the most Generative Ai Testing job openings:

Infographic showing various Generative Ai Testing job openings in Tennessee as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 10% Part Time, 5% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Biometrics AI/ML Engineer

Oak Ridge National Laboratory

Oak Ridge, TN • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted yesterday


Oak Ridge National Laboratory rating

8.8

Company rating: 8.8 out of 10

Based on 16 frontline employees who took The Breakroom Quiz

14th of 120 rated laboratories


Job description

Requisition Id 16775 

Overview:  

Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security. The Human Analysis and Biometrics (HAB) group at Oak Ridge National Laboratory (ORNL) conducts research, development, and deployment of human and group recognition technologies to address national security and other worldwide challenges.

We are seeking a technical contributor in AI/ML research to build models and pipelines, design and implement evaluations and benchmarks, and disseminate results through publications and briefings. You will partner with multidisciplinary researchers across ORNL and external collaborators to enable data-driven decision-making in a variety of contextsThis position resides in the Human Analysis and Biometrics Group in the Advanced Intelligent Systems Section, Cyber Resillience and Intelligence Division, National Security Sciences Directorate, at Oak Ridge National Laboratory (ORNL).

Major Duties/Responsibilities:  

  • Contributes to requirements definition, design, and development of software applications in supporting the needs of projects as directed by the Principal Investigator and/or software team lead.
  • Develop AI/ML models and systems for diverse data and mission contexts
  • Conduct independent and collaborative research in AI/ML, with a focus on multimodal learning, computer vision, and scientific machine learning
  • Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex data, and predictive modeling
  • Design and implement reproducible pipelines for data acquisition, feature engineering, model training, evaluation, packaging, and deployment
  • Help the group envision, design, develop, test, and deploy human analysis and biometric recognition tools and prototypes.
  • Conduct rigorous statistical analysis to aid in data exploration and interpretation
  • Benchmark and T&E AI/ML systems against performance metrics and robustness; define and implement measures of success for deployment-ready systems
  • Disseminate results via technical reports, publications, presentations, and sponsor briefings
  • Visualize and explain complex data and model results to technical and non-technical audiences
  • Contribute to research proposals and statements of work
  • Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success

Basic Qualifications: 

  • A BS degree in computer science, computational science, data science, artificial intelligence, or related field.
  • Experience with agile software development methodologies such as SCRUM.
  • Strong foundation in machine learning, deep learning, or computer vision
  • Strong Python development skills and familiarity with git, CLI tooling, VS Code
  • Proficiency with PyTorch and/or TensorFlow, along with other Python ML development packages; experience building, training, evaluating ML/DL models
  • Experience implementing reproducible data/model pipelines and documenting assumptions, parameters, metrics, and results 

Preferred Qualifications: 

  • Experience with predictive modeling and generative AI/LLMs, including RAG systems
  • Familiarity with LLM inference servers (e.g., vLLM, Ollama)
  • Familiarity with high-performance computing (HPC) and distributed training environments
  • Hands-on benchmarking/Test & Evaluation of AI systems
  • Interest in AI applications for safety, risk modeling, or scientific workflows
  • Excellent written and oral communication skills
  • Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory
  • Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs

Special Requirements:  

  • Q clearance: This position requires the ability to obtain and maintain a clearance from the Department of Energy. As such, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program.

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation. 

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience. 

Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts. 

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov.

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.


If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.


ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.


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