Principal Machine Learning Scientist (alle Geschlechter)
Core
Lead the development, evaluation, and application of machine learning algorithms and workflows for the characterization and de-novo design of biomolecules to accelerate drug discovery.
Role type
Principal Machine Learning Scientist (PhD level)
Builds
ML-based workflows for characterizing biomolecular interactions, drugability assessment, candidate screening, and therapeutic design.
Domain
Biopharma / Computational Chemistry / Drug Discovery
Deliverable
production ML models
Required skills
Machine learning for biomolecular modeling (sequence-to-function, protein language models, co-folding, inverse folding, controllable generative methods), ML workflows for drugability and candidate screening, large-scale data processing (sequence, omics, biochemical, biophysical, structural biology), bioinformatics tools (MMseqs, Foldseek), Python (PyTorch, Pandas, scikit-learn), scientific software development practices.
Preferred skills
Experience in industry post-PhD, collaboration with chemists, biologists, and data scientists.
Technologies
PyTorch, Pandas, scikit-learn, MMseqs, Foldseek
Responsibilities
Develop and evaluate ML algorithms for biomolecule characterization and design; Identify opportunities to accelerate drug discovery projects using internal and external AI capabilities; Communicate technical progress and state-of-the-art to diverse stakeholders; Track advancements in computational modeling of biomolecular structures, physics, interactions, and functions.
Seniority
Principal, hands-on IC with PhD
