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.