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2 weeks ago
We are developing a cutting-edge AI drug discovery engine that combines AlphaFold/OpenFold structural predictions, generative molecular design, and reinforcement learning agents to navigate the vast chemical space.
This expert will partner closely with our Ligand Design Lead to integrate chemical and biological expertise into the model pipeline, ensuring our AI agents produce compounds that are potent, novel, and biologically relevant.
Key Responsibilities:- Design and implement deep learning architectures for protein–ligand pose prediction, pocket-conditioned molecular generation, and multi-objective optimization.
- Fine-tune AlphaFold/OpenFold and related structure models for project-specific targets.
- Integrate multi-modal biological context into generative and scoring models.
- PhD or MSc in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or related discipline.
- 2+ years of hands-on experience in deep learning model development, ideally in a scientific or molecular domain.
- Strong expertise in generative modeling, 3D geometric deep learning, and reinforcement learning.
- Proficiency in programming skills in Python with frameworks like PyTorch or TensorFlow.
Preferred Qualifications:
- Prior work on protein structure prediction, ligand docking, or molecular property prediction.
- Familiarity with cheminformatics toolkits (RDKit, Open Babel).
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