Principal ML Scientist – Predictive Toxicology

📋 Description Lead scientific strategy for predictive toxicology and quantitative biology in drug discovery. Define modelling approaches, endpoints, and data strategies for safety and efficacy decisions. Build and optimize ML models using molecular AI, including graph neural networks, message-passing Apply federated learning for collaborative model development with data privacy. Integrate workflows across multi-omics, image-based screening, and compound prioritization. Collaborate with customers and partners to discuss evaluation, adoption, and roadmap. 🎯 Requirements PhD or equivalent in computational biology, cheminformatics, toxicology, ML, or related field. 6+ years applying ML to drug discovery or life sciences. Strong understanding of deep learning for molecular AI and predictive modelling. Proven experience in predictive toxicity modelling and adoption in pharma/biotech. Knowledge of toxicity workflows (DILI, cytotoxicity, genotoxicity) and toxicology datasets. Experience with RNA-seq, toxicity screening, image-based/HTS data; ability to define scientific 🎁 Benefits Competitive compensation with virtual share options. Fully remote-first with flexibility to work from Europe. Wellbeing budget, mental health support, WFH budget, and co-working stipend. Learning budget, generous holiday allowance, opportunities for European office days. Collaborative, international team with experience from leading organizations.