AI BioPharma, Physics Methods (Affinity)

About the Role

We are looking for a Research Scientist with deep experience applying machine learning techniques to problems in computational chemistry and biology, including protein-ligand affinity prediction. As a member of the Large Quantitative Model (LQM) team, you will develop completely new computational tools to reshape drug discovery. 

What You’ll Do

• Plan and execute molecular dynamics (MD) runs for synthetic data generation.
• Implement expressive and efficient deep-learning architectures.
• Work with a multidisciplinary team of medicinal chemists, computational chemists, physicists, engineers, and machine learning scientists to produce novel models for drug discovery, including for protein-ligand affinity prediction.
• Reproduce and improve upon academic results in the field of ML-enhanced drug discovery.
• Write patents, research papers, and technical documents. Participate and present at international conferences.

About You

• A PhD, in computer science, machine learning, computational chemistry, or any scientific field involving substantial work in the above
• 1-3 years industry and/or postdoctoral experience in ML enhanced biopharma
• Experience with geometric deep learning applied to molecular and biological systems.
• Experience with physics-based simulation methods for molecules and biological systems, such as molecular dynamics and free energy perturbation
• Excellent communication skills
• Superior coding ability in Python and relevant ML frameworks such as PyTorch

The US base salary range for this full-time position is expected to be $167k - $234k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.