Principal ML Scientist – Predictive Toxicology

Tasks

• Apply federated learning for collaborative model development
• Build and optimize machine learning models for molecular AI
• Collaborate with customers to support evaluation and adoption
• Define modelling approaches and data strategies for safety and efficacy
• Integrate multi omics and image based screening workflows into scalable solutions
• Lead scientific strategy for predictive toxicology and quantitative biology
• Mentor scientists and build internal capabilities
• Translate scientific challenges into practical AI for drug discovery

Perks/Benefits

• Co-working stipend
• European office days participation
• Fully remote-first
• Generous holiday allowance
• Learning and professional development budget
• Mental health support
• Wellbeing budget
• Work from home budget

Skills/Tech-stack

Cheminformatics | Compound Prioritization | Computational Biology | Data Privacy | Deep learning | Federated Learning | Graph Algorithms | Graph Neural Networks | High Throughput | High Throughput Screening | Image Based Screening | Language Processing | Machine Learning | Molecular Modeling | Multi-Omics | Natural Language | Natural Language Processing | Neural Networks | Privacy-preserving | Privacy-preserving machine learning | Quantitative biology | RNA-seq | Toxicology | Transformer