Senior Research Scientist, Foundational AI in Health

• Conduct AI research and development in the field of health technology, primarily focusing on wearables and mobile platforms, for tracking and predicting health-related metrics and outcomes.
• Design architectures for multimodal AI models and set up tests to deploy ideas broadly, while building and improving AI research infrastructure by ingesting datasets and maintaining evaluation systems.
• Communicate research findings to a wide variety of audiences, and publish findings in academic journals.
• Identify upcoming research areas by interacting with potential collaborators to help develop long-term research strategy and plans that expand the impact of Google research.
  
• Collaborate with cross-functional partners on engineering, product, and clinical teams to integrate research developments into consumer-facing products.
  

Minimum qualifications:

• PhD degree in Computer Science, Biomedical Engineering, or a related quantitative field.
• 2 years of experience with modern machine learning frameworks such as JAX, PyTorch, or TensorFlow and distributed training on accelerators.
• One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Preferred qualifications:

• 5 years of experience in machine learning research, foundation models (e.g. LLMs, VLMs, time-series foundation models), Generative AI, and coding experience in Python in a team-based environment.
• Experience modeling longitudinal or multimodal real-world data such as wearable sensor data, physiological signals, health records, imaging.
• Experience building data pipelines and experimentation systems for large, heterogeneous datasets.
• Experience designing evaluations, metrics, and controlled ablations that produce reproducible and defensible research results.

• PhD degree in Computer Science, Biomedical Engineering, or a related quantitative field.
• 2 years of experience with modern machine learning frameworks such as JAX, PyTorch, or TensorFlow and distributed training on accelerators.
• One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).