Director, Data Science, Foundation Model AI

This director-level role leads foundational AI and data science efforts focused on training large multi-modal foundation models for biological insight. The team’s work supports disease mechanism understanding and therapeutic target/biomarker discovery through advanced machine learning applied to omics, imaging, and text modalities.

• Lead training of large multi-modal foundation models across omics, imaging, and text modalities
• Provide deep technical guidance and interpret results from machine learning and AI models
• Collaborate with cross-functional teams to prioritize high-impact research questions
• Coach and advance machine learning researchers while cultivating a culture of AI excellence
• Publish findings in conferences and journals and build collaborations with academia and industry

You will oversee foundation model development and bespoke methods for noisy biological data, with deep expertise in classical machine learning such as probabilistic models and causal analysis. The role includes interpreting model outputs, providing technical guidance, and training multi-modal models using high-performance GPU clusters.

The ideal candidate is a senior/lead AI research and data science leader with a PhD (or MS with equivalent experience) and 7+ years of full-time experience developing foundation model methods. They have world-class classical machine learning expertise in probabilistic models and causal analysis, with strong research credibility demonstrated through publications at NeurIPS/ICML/ICLR/AISTATS and/or open-source projects, and experience training multi-modal foundation models on biological omics, imaging, and text data.