AI Researcher, Foundation Models

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Who We Are

Verily is a subsidiary of Alphabet that is using a data-driven approach to change the way people manage their health and the way healthcare is delivered. Launched from Google X in 2015, our purpose is to bring the promise of precision health to everyone, every day. We are focused on generating and activating data from a variety of sources, including clinical, social, behavioral and the real world, to arrive at the best solutions for a person based on a comprehensive view of the evidence.

Our unique expertise and capabilities in technology, data science and healthcare enable the entire healthcare ecosystem to drive better health outcomes.

As an AI Researcher on our new Foundation Models team, you will help build the next generation of AI that understands human health at a deep, multimodal level. Our mission is to develop foundational models that integrate diverse, large-scale health data—including structured EHR, unstructured clinical notes, genomics, and wearable sensor data—to unlock novel insights and power future clinical and research applications.

In this role, you will design novel deep learning architectures, conduct applied research in self-supervised and multimodal learning, and translate your findings into robust, scalable models. You will focus on building representations that capture the complex, longitudinal nature of patient health, creating a core asset that will accelerate discovery and product development across the organization.

Success in this role requires scientific creativity, a strong sense of self-initiative, and pragmatic engineering. You’ll be exploring the state of the art in foundation models (including LLMs and multimodal architectures) while ensuring these models are reliable, interpretable, and adaptable for a wide range of downstream clinical and research applications.

• Design and develop large-scale foundation models and self-supervised learning algorithms to integrate and learn from complex, multimodal health data (e.g., structured EHR, unstructured text, genomics, wearables).
• Proactively explore, benchmark, and validate new modeling architectures and learning techniques for complex, longitudinal health data.
• Communicate complex technical concepts and results clearly, adapting style and depth for both technical and non-technical audiences. Partner closely with clinical experts, product managers, and stakeholders to define problems, identify data needs, and ensure solutions are clinically relevant.
• Stay current with advancements in AI/ML research (especially in foundation models, LLMs, and multimodal learning) and identify opportunities to apply them within biomedical and health contexts.
• Contribute to an inclusive, collaborative team environment where diverse perspectives are valued and leveraged, especially in a new and exploratory team setting.

• Advanced degree in a quantitative discipline (e.g., data science, computer science, biomedical informatics, statistics, applied mathematics, or similar), or equivalent practical experience.
• 5+ years of experience developing and applying advanced deep learning, self-supervised learning, and foundation models (including LLMs) to complex, large-scale data (e.g., clinical, biomedical, genomic, or time-series data).
• Strong proficiency in Python and experience with modern deep learning frameworks (e.g., PyTorch, Huggingface/Transformers, Tensor Flow) and Git-based workflows.
• Demonstrated ability to design and implement novel algorithms or adapt cutting-edge research methods for practical applications.
• Excellent written and verbal communication skills, with a proactive, collaborative approach to problem-solving and navigating ambiguity.

• Familiarity with medical terminologies and standards (e.g., ICD, CPT, SNOMED, FHIR, OHDSI/OMOP).
• Experience collaborating with clinical professionals, bioinformaticists, or other health domain experts.
• Exposure to MLOps and software engineering best practices for building and deploying large-scale models.
• A strong sense of curiosity, adaptability, and…