Clinical Specialist, AI/ML, Health Optimization
GoogleMinimum qualifications:
- Doctoral degree in a clinical field (e.g., PhD, MD, DO, DNP, PsyD, PharmD), or a related health field such as Nursing Informatics, Biomedical Informatics, Human-Computer Interaction, Biostatistics, Behavior Science, Computer Science.
- 3 years of experience in AI/ML applied to health, developing methods for evaluating and mitigating AI model performance, including disaggregated evaluation, auditing, and benchmarking.
- 3 years of experience in clinical or public health program implementation or research.
- 3 years of experience applying frameworks that integrate social sciences, human context, or public health principles into technology development.
Preferred qualifications:
- 5 years of experience in patient care after completing a clinical degree.
- Experience leading applied research in Health AI with a focus on product development.
- Experience working in dynamic environments with cross-functional product development teams and with deep learning frameworks.
- Knowledge of behavioral science, ecological systems, or community engagement methodologies in health technology and applying these theories in practice.
- Proficiency in Python or SQL for implementing quantitative evaluation pipelines (e.g., for statistical or epidemiological analysis).
- Ability to 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'll be an influential contributor, embedding human context into the early stages of Artificial Intelligence (AI) development to support research programs and products. With a data-driven, proactive approach, you will guide the creation and evaluation of GenAI solutions to achieve meaningful health impact on a global scale.
You will leverage your clinical and AI skills, knowledge of the research-to-product life-cycle, and communication and leadership skills to shape the design and implementation of GenAI solutions. You will be experienced in health AI research, development, and deployment, with the ability to work cross-functionally with non-clinical teams (e.g., engineering, product management, UX/UXR, legal, and regulatory affairs). You will have a demonstrable background as a professional with recognized skills 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 vision 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.The US base salary range for this full-time position is $146,000-$214,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Provide clinical leadership using your AI, research, and product skills to shape product and research roadmaps across Google.
- Work 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.
- Develop data pipelines to manage the life-cycle of health datasets, ensuring curation and analysis are grounded in human context to drive meaningful insights.
- 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.
- Develop and apply 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.