Machine Learning Engineer, Causal Discovery

SandboxAQ’s AI Simulation team is advancing the frontiers of drug and materials discovery by integrating physics-based simulations with cutting-edge AI. As a Machine Learning Engineer you will drive causal inference capabilities across complex biological systems using multi-modal datasets—including omics data, clinical information, and physics-based simulations. In this role, you will design and build causal machine learning systems that enable a deeper understanding of biological mechanisms and accelerate scientific discovery. You will apply advanced graph-based reasoning techniques—including Graph Neural Networks, Probabilistic Graphical Models, and LLMs—for querying and inference over large-scale causal biomedical knowledge graphs constructed from simulation, omics data, and literature. You will also research and prototype novel bioinformatics and deep learning approaches to interpret human genetic variants, gene regulation mechanisms, gene expression dynamics, and disease pathways using diverse multimodal data (e.g., clinical phenotypes, medical records, multi-omics, single-cell data, proteomics, genomics).