Clinical Specialist, Health Optimization

Job description

Contribute clinical and methodological expertise using AI, research, and product expertise to shape product and research roadmaps across Google. Collaborate effectively with research, engineering, product, and UX teams to guide the integration of human context, including social and structural factors into GenAI model development, evaluation, and product design. Support the development of robust data pipelines to manage the end-to-end life-cycle of complex health datasets, ensuring curation and analysis are grounded in human context to drive meaningful insights. Pinpoint and implement technical interventions at critical junctures of the model development life-cycle, from pre-training dataset curation to product deployment to ensure effective and impactful health AI solutions. Apply comprehensive knowledge to execute methodologies for evaluating AI model performance across various populations, combining machine learning, social science, and public health principles to build scaled approaches to health AI. Minimum Qualifications: Doctoral degree in a clinical field (e.g., MD, DO, MBBS, PharmD, DNP, PsyD, PhD). 2 years of experience in patient care. 1 year experience applying frameworks that integrate social sciences, human context, or public health principles into technology development. 1 year experience with evaluation of AI product or digital health product development. Preferred Qualifications: Advanced computational degree (e.g., PhD, MPH, MS) with applied experience in health, such as nursing informatics, biomedical informatics, human computer interaction, biostatistics, behavior science, or computer science. Experience in clinical practice in global settings or treating patients across the lifecourse, including pediatric and geriatric populations. Computational skills in AI/ML applied to health, developing and using methods for evaluating and mitigating AI model performance, including disaggregated evaluation and benchmarking. Background in medical anthropology, sociology, ecological systems, or other sociobehavioral sciences. Demonstrated track record of health-related publications or contributions to AI/ML products in the health domain.