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 to improve safety and efficacy Build and optimize ML models using molecular AI techniques (graph nets, transformer-based models). Apply federated learning for collaborative model development with privacy guarantees. Integrate multi-omics, image-based screening, and high-throughput workflows into scalable solutions. Collaborate with customers and partners to drive adoption, evaluation, and roadmaps. 🎯 Requirements PhD or equivalent in computational biology, cheminformatics, toxicology, ML, or related field. 6+ years applying ML to drug discovery or life sciences. Strong DL methods for molecular AI and predictive modelling. Proven experience building predictive toxicity models and driving adoption. Knowledge of toxicity workflows (DILI, cytotoxicity, genotoxicity). Experience with biological datasets (RNA-seq, toxicity screens, high-content imaging). 🎁 Benefits Competitive compensation with virtual share options. Fully remote-first model with location flexibility. Wellbeing budget and mental health support. Work-from-home stipend and professional development budget. Generous holiday allowance and opportunities for office days in Europe. Collaborative, international team environment.