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.