Director, AI and Machine Learning for Biology

About the Role

DARe is building an AI-enabled discovery engine that integrates longitudinal human data, genetics, multi-omics, imaging, and advanced biological models to uncover causal drivers of aging, resilience, and regeneration. Our goal is to translate these insights into differentiated novel therapeutic targets and medicines for age-related diseases.

We are seeking an exceptional scientific and technical leader to shape DARe's global Data Science & AI strategy and build our US capability in San Diego. The successful candidate will combine strategic leadership with technical depth to guide high-impact AI programs, connect computational and experimental science, and translate emerging capabilities into meaningful drug-discovery decisions.

Key Responsibilities

• Lead the US DARe Data Science & AI efforts and shape the global AI and ML strategy
• Define a prioritized portfolio of Data Science & AI initiatives, establish clear measures of scientific and operational impact, and ensure that investments translate into better target decisions, stronger experimental design, and accelerated discovery
• Co-shape and drive data science and AI efforts supporting target identification, target prioritization, and validation across disease programs and contribute to the DARe long-term scientific AI vision
• Lead the development and application of biological foundation models, multimodal learning, biomedical knowledge graphs, and agentic workflows to drive evidence integration and target prioritization
• Work at the intersection of human genetics, longitudinal clinical trajectories, proteomics, transcriptomics, metabolomics, imaging, and electronic health record data
• Build, lead, and develop a high-performing US-based team spanning applied AI, machine learning, computational biology, and data science, while creating meaningful growth opportunities for early-career talent
• Evaluate emerging technologies and establish external partnerships that accelerate DARe's AI capabilities
• Translate scientific questions into computational strategies, selecting fit-for-purpose statistical, machine learning, AI, and bioinformatics approaches
• Build strong organizational connectivity to ensure effective collaboration, knowledge sharing, and execution and foster a one-team culture across geographies and scientific disciplines
• Drive AI adoption across the department
• Partner closely with internal functions including Data and Digital, Discovery Sciences and Disease areas

Essential Requirements

• PhD in Machine Learning, Computational Biology, Data Science, Computer Science, Systems Biology or a related quantitative discipline combined with a deep understanding of biology
• 10+ year experience applying advanced computational and AI approaches to biomedical research, drug discovery, or translational medicine
• Proven success leading multidisciplinary global teams across AI, human data, and experimental science
• Demonstrated experience developing or applying advanced machine-learning approaches to complex biological data, such as foundation models, representation learning, generative models, or multimodal learning across sequence, single-cell, spatial, imaging, or other biomedical data
• Strong working knowledge of deep learning, representation learning, multi-modal AI, generative AI, large-scale biological data modeling
• Strong experience in large-scale model training, distributed computation, model adaptation, and deployment within machine learning operations frameworks
• Strong coding proficiency in Python and deep learning frameworks, with experience using version control systems
• Experience with omics data (transcriptomics, proteomics, metabolomics) and cellular imaging data
• Ability to lead and influence across matrix organizations with exceptional communication and stakeholder management skills
• Demonstrated ability to operate effectively in a fast-paced and highly collaborative research environment

Desirable Requirements

• Expertise in statistical genetics, including genome-wide association studies, rare variants, Mendelian randomization, colocalization, or fine-mapping
• Understanding of modern data foundations including ETL/ELT pipelines, biomedical ontologies, metadata standards, data governance, and FAIR data principles
• Expertise in diseases of aging, neurodegeneration, cardiometabolic disease, regeneration biology, or related therapeutic areas
• Experience with knowledge graph technologies and scientific knowledge integration platforms
• Experience with developing biological aging clocks