Interest in the application of research to healthcare.

About the job

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

Health AI's mission is to catalyse the adoption of human-centred AI in healthcare and medicine. We see AI as transformational in these fields, and a necessary component in realizing personalized medicine, expanding access for underserved populations, improving quality and consistency, deploying care at the edge, and closing the loop of a learning healthcare system.

The Health AI India team conducts machine learning (ML) research as part of a broader multidisciplinary effort. As part of this team in Health AI, you'll have opportunities to advance foundational ML capabilities (e.g., multimodality, reasoning, safety, interpretability, memory) and build end-to-end agents to enable breakthrough capabilities, and pioneer next-generation experiences in collaboration with other teams in consumer health and adjacent domains such as education.

Responsibilities

• Innovate, implement and evaluate machine learning algorithms over multimodal healthcare data.
  
• Author publications of novel research in leading scientific journals and conferences.
  
• Design, implement and deliver complex pieces of research and infrastructure as part of a multi-disciplinary team.
  
• Manipulate and evaluate large and complex medical datasets.
  
• Work with a variety of internal and external stakeholders, including Product Managers, Clinicians, UX researchers and Regulatory Advisors.