Senior Scientist, Machine Learning (Biologics Design)
Core
Develop and apply machine learning methods for the design and optimization of large molecules, including antibodies and multispecifics, to guide lead optimization decisions.
Role type
Senior Scientist, Machine Learning (Biologics Design)
Builds
Predictive and generative models for sequence and structure design of protein therapeutics
Domain
Biopharmaceuticals / Protein Therapeutics / Structural Biophysics
Deliverable
production ML models
Required skills
Python, PyTorch, JAX, NumPy, pandas, deep learning model architecture, representation learning, multimodal learning, geometric deep learning, generative modeling, protein structure knowledge, antibody architecture, biophysical principles
Preferred skills
Molecular modeling (Amber, OpenMM, Rosetta, CHARMM), production-grade ML tooling, industry experience in biologics discovery
Technologies
PyTorch, JAX, NumPy, pandas, Amber, OpenMM, Rosetta, CHARMM
Responsibilities
Develop ML models for biologics design (sequence-to-function, structure-aware, multi-objective); Implement data-efficient modeling strategies (active learning, Bayesian optimization); Apply deep learning approaches (protein language models, geometric deep learning, diffusion, inverse folding); Perform structure-based modeling of antibodies and multispecifics; Partner with experimental teams to translate computational results into decisions
Seniority
Senior, hands-on IC
