AI Research Scientist

• Own research problems in predictive toxicology, drug safety, and computational biology from hypothesis through experimental validation
• Develop and evaluate novel deep learning architectures and training methods, including graph neural networks, transformers, multimodal models, and generative approaches
• Investigate representations connecting molecular structure, biological targets, pathways, dose and exposure, pharmacology, toxicology, and clinical outcomes
• Develop approaches for heterogeneous, sparse, noisy, and partially observed scientific datasets
• Explore cross-domain and out-of-distribution generalization for novel compounds, chemical spaces, biological contexts, and endpoints
• Design experiments, benchmarks, ablations, and evaluation frameworks
• Develop uncertainty estimation, calibration, applicability-domain assessment, and confidence-aware prediction methods
• Make model predictions more mechanistically interpretable
• Explore multimodal and foundation-model approaches combining chemical, biological, experimental, literature-derived, and clinical evidence
• Identify model limitations and failure modes and turn them into research directions
• Work with AI/ML and data engineers to translate research into reproducible, scalable modeling systems
• Contribute to scientific strategy, validation studies, external collaborations, publications, and regulatory evaluation support
• Collaborate closely with the CTO, engineers, data engineers, and scientists