Healthcare Artificial Intelligence Evaluation and Risk Assessment Specialist

Tunnell Government Services, Inc., is comprised of the industry’s leading scientists, medical and technical experts, and human capital and organizational development professionals. We approach all projects as a team and are committed to helping our clients transform and quickly achieve strategic goals. If you are interested in joining an employee-owned company that develops and implements innovative, integrated and differentiated solutions, please click on the links below to find the job that is right for you and contact us. Title: Healthcare AI Evaluation and Risk Assessment Specialist Job Type: Full Time Employee Schedule: Monday through Friday (no Holidays) Location: This position is 100% telework Digital Health Center of Excellence (DHCoE), Office of Strategic Partnerships and Technology Innovation (OST), Center for Devices and Radiological Health (CDRH), FDA Position Summary The Healthcare AI Evaluation and Risk Assessment Specialist provides scientific, technical, and regulatory expertise supporting FDA's oversight of AI-enabled medical devices. The incumbent serves as a subject matter expert in the evaluation of emerging healthcare AI technologies, including generative AI, large language models (LLMs), foundation models, multimodal AI systems, and agentic AI, and their application in medical devices across clinical specialties. The position focuses on evaluating safety, effectiveness, performance, reliability, risk management, and evidence supporting AI-enabled medical devices and contributing to regulatory science, policy development, and review best practices. [fda.gov], [fda.gov] Major Duties And Responsibilities Review premarket submissions, IDE applications, and other regulatory submissions involving AI-enabled medical devicesEvaluate AI model performance, validation strategies, benchmarking methodologies, and supporting evidence for safety and effectivenessAssess AI risk assessment and risk management approaches, including model failure modes, hallucinations, bias, automation bias, cybersecurity risks, human factors, post-market monitoring, and lifecycle managementEvaluate model testing approaches, including benchmarking, robustness testing, subgroup analyses, uncertainty quantification, stress testing, and adversarial testing (red teaming)Identify scientific limitations of proposed evaluation designs, datasets, scoring methodologies, and validation approachesAssess evidence from clinical studies, reader studies, simulation-based evaluations, real-world evidence, and external validation studiesProvide scientific consultation to multidisciplinary review teams on AI evaluation methodologies, evidence generation, transparency, explainability, governance, and performance monitoringCollaborate with FDA staff, other Federal agencies, academic institutions, healthcare organizations, standards development organizations, and industry stakeholdersContribute to the development of regulatory science, policy, guidance documents, review methodologies, and best practices related to healthcare AI and emerging digital health technologiesMonitor advances in AI technologies and identify scientific challenges and opportunities relevant to FDA's regulatory missionPrepare technical reports, scientific reviews, briefing materials, presentations, and policy analyses Minimum Qualifications Master's degree or higher in artificial intelligence, machine learning, computer science, data science, biomedical engineering, health informatics, medicine, public health, or a related fieldAt least seven (7) years of relevant experience in healthcare AI, medical devices, digital health, regulatory science, or related disciplinesDemonstrated expertise in AI evaluation, risk assessment, evidence generation, and performance assessment methodologies Preferred Qualifications PhD, MD, DVM, PharmD, or other advanced scientific degreeExperience supporting FDA or other Federal healthcare agenciesKnowledge of:Generative AI and LLMsFoundation and multimodal modelsSoftware as a Medical Device (SaMD)Medical device regulatory pathways (510(k), De Novo, PMA, IDE)AI governance and Good Machine Learning Practice (GMLP)Medical device cybersecurity and real-world evidence Must be a US Citizen or Full Green Card holder. Tunnell strives to hire and retain the most qualified talent. We believe that having employees with different backgrounds and capabilities contributes to the success of our business. To attract qualified applicants, Tunnell posts to a wide variety of job sites. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Please be advised that certain client contracts may outline specific requirements for onsite work. Tunnell employees and/or contractors are expected to comply with all client requirements, including any safety and health protocols.