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3 days ago
Key to unlocking the full potential of our cutting-edge AI drug discovery engine is a seasoned Deep Learning Architect. This individual will oversee the development and deployment of machine learning models for protein–ligand structure prediction, de novo molecular generation, and multi-objective optimization.
We are seeking an expert with hands-on experience in deep learning model development, particularly in scientific or molecular domains. The ideal candidate will have strong expertise in generative modeling, 3D geometric deep learning for molecular structures, and reinforcement learning.
The chosen candidate will be responsible for designing and implementing deep learning architectures for:
- Pocket-conditioned molecular generation (e.g., Pocket2Mol, SE(3)-equivariant GNNs, 3D diffusion models).
- Protein–ligand pose prediction (DiffDock, EquiBind, custom SE(3)-transformers).
- Multi-objective reinforcement learning for compound optimization (potency, ADMET, novelty).
Responsibilities also include fine-tuning AlphaFold/OpenFold and related structure models for project-specific targets, integrating multi-modal biological context into generative and scoring models, and developing scoring functions for binding affinity, selectivity, ADMET, and synthetic accessibility.
To excel in this role, you should possess:
- A PhD or MSc in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or related discipline.
- At least two years of experience in deep learning model development.
- Strong programming skills in Python with frameworks like PyTorch or TensorFlow.
In addition, familiarity with cheminformatics toolkits (RDKit, Open Babel) and experience in handling molecular datasets (PDB, ChEMBL, binding affinity data) is highly desirable.
You will join a team of innovators pushing the boundaries of molecular AI. Your work will contribute directly to the development of our proprietary agentic search system designed to explore the chemical universe faster and smarter than competitors.
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