Director, Translational AI & Mechanistic Biology in Cambridge

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being outstanding connectors: you'll actively harness existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn sophisticated, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real!

We are seeking a talented and scientifically ambitious AI expert to join our Translational AI team, applying cutting-edge AI approaches to deepen our understanding of disease biology and therapeutic response. This role will focus on uncovering the biological mechanisms that drive treatment response, resistance, and disease progression by integrating multimodal data across discovery, translational, and clinical settings. The successful candidate will work at the intersection of AI, human biology, and drug development, partnering closely with translational scientists, clinicians, bioinformaticians, and disease-area experts. This is an exciting opportunity to help shape the future of AI-enabled translational science and contribute directly to the development of next-generation medicines.

What You'll Do

• Advance mechanistic understanding of disease and therapeutics
• Apply AI and computational approaches to elucidate disease mechanisms, drug mechanisms of action, and mechanisms of resistance.
• Integrate molecular, cellular, omics, imaging, and clinical data to generate novel biological insights.
• Develop models that connect targets, pathways, biomarkers, patient characteristics, and clinical outcomes, enabling forward and back translation.
• Support programme teams in understanding therapeutic response and identifying opportunities for patient stratification based on mechanistic understanding.
• Develop innovative AI approaches
• Apply modern machine learning and AI approaches to translational science challenges.
• Explore emerging techniques including multimodal modelling, biological foundation models, causal inference, systems biology, and knowledge-based reasoning.
• Build analytical frameworks that can be deployed across multiple therapeutic areas and programmes.
• Develop reusable AI methods, platforms, and capabilities that can be adopted across multiple therapy areas, enabling scalable impact and reducing duplication of effort.
• Translate advances in AI into practical applications that accelerate decision-making in R&D.
• Collaborate across scientific disciplines
• Partner with translational medicine, clinical development, data science, bioinformatics, and laboratory scientists to address critical scientific questions.
• Contribute computational and mechanistic expertise to cross-functional project teams.
• Help identify opportunities where AI can provide unique insights into disease biology and therapeutic response.
• Communicate findings effectively to both technical and non-technical stakeholders.
• Contribute to scientific excellence
• Drive high-quality scientific research and innovation.
• Publish and present methods and findings internally and externally; contribute first- or last-author publications in leading ML and clinical AI journals where appropriate.
• Mentor junior scientists and contribute to developing best practices within the organisation.
• Stay at the forefront of advances in AI, computational biology, and translational medicine.

Essential Requirements

• PhD (or equivalent experience) in Machine Learning, Data Science, Computational Biology, Systems Biology, or a closely related quantitative discipline — with a strong, hands-on computational track record.
• Demonstrated experience of high-impact application of AI/ML methods to biological, translational, or clinical data.
• Deep knowledge of one or more areas of modern AI: foundation model training and fine-tuning adaptation (e.g. LLMs, multimodal and biological foundation models), model interpretability and explainability, causal machine learning, graph machine learning, agentic AI.
• Exceptional software engineering skills: Python, deep learning frameworks (e.g. PyTorch), frontier coding agent frameworks, modern LLM tooling, and cloud platforms (e.g. AWS, Azure, GCP).
• Demonstrated ability to generate novel insights from complex biological datasets.
• Experience collaborating effectively across multidisciplinary scientific teams.
• Excellent communication skills and the ability to explain complex concepts clearly.
• Peer-reviewed publications in clinical AI, computational drug development, or leading ML venues (e.g. NeurIPS, ICML, ICLR).

Desirable Skills/Experience

• Strong understanding of human disease biology and the generation, analysis and interpretation of translational datasets, including genomics, transcriptomics, proteomics, single-cell and spatial biology, clinical biomarkers, pathology imaging, longitudinal clinical data / real-world data and biomedical knowledge graphs.
• Understanding of pharmaceutical research and drug development, from target validation through clinical development, including the role of translational science in patient stratification, biomarker strategy and decision making.
• Experience in one or more of the following areas is desirable: mechanism of action (MoA) studies, biomarker discovery, mechanisms of resistance (MoR), patient stratification, network and systems biology, causal inference, or multimodal disease modelling.

What Success Looks Like

• Deliver new insights into disease mechanisms and therapeutic response.
• Help explain why drugs work, why resistance emerges.
• Establish AI-driven approaches that improve decision-making across the drug development lifecycle.
• Become a recognised scientific expert at the intersection of AI and translational biology.
• Contribute to building a leading Translational AI capability within R&D.

Why this role matters

Understanding disease is no longer limited by data generation. The challenge is turning increasingly rich biological and clinical data into mechanistic insight. In this role, you will use AI to bridge that gap, helping reveal the biological processes that drive patient outcomes and ultimately enabling the development of more precise and effective medicines.