Scientific Machine Learning Engineer

Role: Scientific Machine Learning Engineer focused on developing scalable software for ML-driven virtual screening in drug discovery. Responsibilities: design and implement ML workflows integrating physics-based methods (docking, free energy calculations), collaborate with scientists to adapt workflows for new discovery cases, and explore novel ML approaches to enhance hit identification. Requirements: Bachelor's or higher in Chemistry, Physics, CS, or related; 1-3 years of production-level software experience; proficiency in Python and ML frameworks like PyTorch; prior ML experience in chemical or biological property prediction. Preferred: familiarity with molecular dynamics, free energy calculations, cheminformatics, quantum mechanics, or related computational chemistry software. HighValue: computational chemistry, ML applications in drug discovery, physics-based integration, virtual screening, hit-to-lead optimization, research collaboration. WorkSetup: Not specified; likely hybrid or remote based on typical roles.