Senior Applied AI Researcher, Digital Biology

Responsibilities

• Conceptualize, design, and implement novel deep learning architectures for biological data, with a strong focus on large-scale models such as Large Language Models (LLMs), Transformers, and State Space Models (SSMs).
• Develop multimodal learning systems that integrate heterogeneous data types (e.g., clinical time-series, imaging, genomics, and text) for improved representation and prediction.
• Develop both foundational and generative models and agentic AI systems, including multi-step reasoning, tool use, and autonomous decision-making capabilities.
• Develop digital twin systems for healthcare, combining mechanistic models, physiological data, and AI to simulate disease progression, treatment response, and patient-specific trajectories.
• Implement deep learning systems integrated with agents, enabling end-to-end workflows that combine learning, planning, and execution.
• Evaluate model performance, analyze results, and iterate on designs to achieve optimal outcomes.
• Apply your knowledge of distributed training to build high-quality code for training, optimizing, and deploying large-scale models, while managing complex datasets.
• Collaborate closely with a diverse team of researchers, bioinformaticians, and domain experts in a highly interdisciplinary environment.
• What We Need to See
• We are looking for individuals who demonstrate a strong foundation in deep learning and a proven ability to translate innovative ideas into practical, scalable systems.

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