Principal ML Scientist – Predictive Toxicology
Core
Own and grow the expansion into small molecule predictive toxicology and quantitative biology, integrating scientific workflows into a federated learning platform to build high-performance models for drug discovery.
Role type
Principal scientific leader (hands-on IC)
Builds
Federated machine learning models for predictive toxicology and quantitative biology
Domain
Life sciences / Biopharma / Computational toxicology
Deliverable
production ML models
Required skills
Deep learning for molecular AI (GNNs, transformers), predictive toxicology modeling, quantitative biology, federated learning, multi-omics integration, high-throughput screening (HTS), image-based screening
Preferred skills
Prospective validation of toxicity models, production-grade model delivery in pharma, multi-omics and high-content imaging experience, familiarity with public toxicology data resources (Tox21, ToxCast)
Technologies
Graph neural networks, message-passing networks, transformer-based models, federated learning frameworks
Responsibilities
Set scientific strategy for in silico toxicology workflows, decide on data usage to extract commercial value, span multiple scientific surfaces from off-target liability to in vivo pharmacokinetics, build models using partner data, lead scientific conversations with customers and partners
Seniority
Principal, scientific leadership with hands-on modeling
