Director, Translational AI & Mechanistic Biology Full Time on the ground NEW

Overview

In this role you apply cutting-edge AI to deepen understanding of disease biology and therapeutic response. You will integrate multimodal data across discovery, translational, and clinical settings to uncover mechanisms driving treatment outcomes. You’ll collaborate with translational scientists, clinicians, and bioinformaticians to build reusable AI capabilities that scale across programs. Your work helps shape AI-enabled translational medicine and next-generation medicines, delivering mechanistic insights and decision-support across drug development.

Pay / Benefits

• competitive salary
• excellent benefits
• inclusive and equitable environment
• reasonable adjustments/accommodations
• sponsorship/support for relocation or work authorization
• career development opportunities

Responsibilities

• Advance mechanistic understanding of disease and therapeutics using AI/ML
• Integrate molecular, cellular, omics, imaging, and clinical data to gain biological insights
• Develop models linking targets, pathways, biomarkers, and clinical outcomes for translation
• Support programme teams in patient stratification based on mechanistic insights
• Develop and apply modern AI approaches (multimodal modelling, causal inference, graph ML, biological foundation models)
• Create reusable AI methods, platforms, and capabilities for cross-therapy adoption
• Translate AI advances into practical decision support to accelerate R&D decisions
• Collaborate with multidisciplinary teams and communicate findings to technical and non-technical stakeholders
• Publish and present methods and findings; mentor junior scientists
• Stay at the frontier of AI, computational biology, and translational medicine

Key requirements

• PhD or equivalent with strong computational track record
• Proven high-impact application of AI/ML to biological or translational/clinical data
• Deep knowledge of foundation models, multimodal AI, interpretability, causal ML, graph ML, and related areas
• Exceptional software engineering: Python, PyTorch, modern LLM tooling, cloud platforms (AWS, Azure, GCP)
• Ability to generate novel insights from complex biological datasets
• Experience collaborating across multidisciplinary teams
• Excellent communication skills
• Peer-reviewed publications in clinical AI, computational drug development, or ML venues

• Strong written and verbal communication
• Cross-functional collaboration
• Mentorship and guidance to junior scientists
• Python
• PyTorch
• Foundation models (LLMs, multimodal, biological foundation models)

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