Job Description
Role: AI Drug Discovery Lead
Pattern is building a lean, AI-driven drug discovery engine.
We are seeking a hands-on AI Lead to own all AI strategy and execution across the de novo drug discovery pipeline , working directly with a medicinal chemist who provides chemical constraints and final decision-making.
Responsibilities
Own the AI components of the full 9-step discovery pipeline, from target selection to docked, synthesis-ready molecules.
Generate and refine protein structures and binding pockets (AlphaFold/OpenFold).
Build and run generative and reinforcement-learning models for de novo molecule design.
Implement docking, scoring, and ranking workflows to prioritize compounds.
Translate chemist-defined constraints into model objectives and filters.
Incorporate chemist feedback and experimental data into continuous model improvement.
Maintain reproducible, automated ML pipelines.
Requirements
MSc or PhD in Machine Learning, Computer Science, Computational Chemistry, or related field.
2+ years hands-on experience building deep learning models.
Strong Python and PyTorch (or equivalent) experience.
Background in generative models and optimization (RL, diffusion, GNNs).
Experience working with molecular or structural biology data.
Why This Role
You will own the AI engine at the core of Pattern’s platform, working in real time with a chemist to turn AI-generated designs into real compounds. This is a high-ownership role focused on execution, speed, and real-world impact—not academic research.
Pattern is building a lean, AI-driven drug discovery engine.
We are seeking a hands-on AI Lead to own all AI strategy and execution across the de novo drug discovery pipeline , working directly with a medicinal chemist who provides chemical constraints and final decision-making.
Responsibilities
Own the AI components of the full 9-step discovery pipeline, from target selection to docked, synthesis-ready molecules.
Generate and refine protein structures and binding pockets (AlphaFold/OpenFold).
Build and run generative and reinforcement-learning models for de novo molecule design.
Implement docking, scoring, and ranking workflows to prioritize compounds.
Translate chemist-defined constraints into model objectives and filters.
Incorporate chemist feedback and experimental data into continuous model improvement.
Maintain reproducible, automated ML pipelines.
Requirements
MSc or PhD in Machine Learning, Computer Science, Computational Chemistry, or related field.
2+ years hands-on experience building deep learning models.
Strong Python and PyTorch (or equivalent) experience.
Background in generative models and optimization (RL, diffusion, GNNs).
Experience working with molecular or structural biology data.
Why This Role
You will own the AI engine at the core of Pattern’s platform, working in real time with a chemist to turn AI-generated designs into real compounds. This is a high-ownership role focused on execution, speed, and real-world impact—not academic research.
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