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Clinical Specialist, Health Optimization
GoogleTel Aviv, Israel2+ years
Apply 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.
About the job
As a Clinical Specialist for Health Optimization, you will plan and execute against project goals to embed human context into the early stages of AI development to support research programs and products. You will have h a data-driven, proactive approach, support the creation and evaluation of GenAI solutions to achieve meaningful health impact on a global scale.In this role, you will use your clinical and AI expertise, knowledge of the research-to-product life-cycle, and strong communication and leadership skills to contribute to the design and implementation of GenAI solutions. You have experience in health AI research, development, and deployment, with expertise in working cross-functionally with non-clinical teams (engineering, product management, User Experience/User Experience Research (UX/UXR), legal, and regulatory affairs). You will have a demonstrable background as a professional with solid expertise in a clinical or advanced health research field and experience evaluating AI models.Google for Health is a company-wide effort to help billions of people be healthier. We work toward this goal by meeting people in their everyday moments and empowering them to stay healthy and partnering with care teams to provide more accurate and accessible care. Our teams are applying our expertise and technology to improve health outcomes globally – with high-quality information and tools to help people manage their health and wellbeing, solutions to transform care delivery, research to catalyze the use of artificial intelligence for the screening and diagnosis of disease, and data and insights to the public health community.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $126000 - $180000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- 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.
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