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Scientist / Senior Scientist, Structure-Based Modeling at Deep Origin. Deep Origin is seeking a Scientist or Senior Scientist with strong expertise in structure-based drug design, including docking, molecular dynamics (MD), and free energy perturbation (FEP), to support a transformative ARPA-H initiative. You'll lead the design of robust simulation workflows and analyze protein-ligand structures across a large target panel to support predictive modeling for therapeutic discovery.. . Ph.D. in computational chemistry, structural biology, biophysics, or related field;. . 2+ years of postdoctoral or industry experience in structure-based modeling;. . Hands-on expertise with FEP (RBFE/ABFE), including best practices around setup, sampling, and analysis;. . Proficiency with one or more simulation platforms (e.g., Schrödinger FEP+, OpenMM, GROMACS, AMBER, NAMD);. . Strong understanding of protein-ligand binding, structure selection, and conformational variability;. . Programming experience in Python, and familiarity with tools like MDAnalysis, PyMOL APIs, or MDTraj;. . Fluent English for collaboration with an international team;. . Ability to work on US time zones when needed.. . Nice to have:. . Experience benchmarking across multiple PDB entries or conformational states;. . Prior work integrating structural modeling into machine learning pipelines;. . Familiarity with MM/GBSA, docking scoring functions, or clustering methods;. . Experience using Unix-based HPC environments, workload managers (e.g., SLURM, etc.), and optionally AWS;. . Comfort managing large-scale simulation data for modeling or analysis.. . Responsibilities:. . Analyze tens to hundreds of protein targets relevant to ADMET and off-targets, focusing on conformations, binding site flexibility, and ligand-bound states to guide structure preparation and ensemble design;. . Run and refine docking, MD, and FEP (RBFE and ABFE) simulations using state-of-the-art tools;. . Apply methods such as restraints, alchemical transformations, and sampling strategies to ensure robust and reproducible FEP workflows;. . Curate, benchmark, and select optimal protein-ligand structures (e.g., from PDB) for predictive modeling;. . Evaluate multiple structural representations (e.g., different PDB IDs) to determine the best input per target;. . Collaborate with cheminformatics, ML, and experimental teams to integrate structure-based insights across discovery pipelines;. . Communicate progress, technical findings, and challenges across internal and external teams.. . Why Join Deep Origin. Deep Origin builds modern infrastructure for computational science at the interface of biology, chemistry, and AI. As part of our ARPA-H program, you’ll shape the future of structure-based modeling for therapeutics.. We offer:. . A remote-first team across the US, Europe, and Armenia;. . Competitive salary and equity packages;. . Flexible working hours;. . A mission-driven, scientifically rigorous culture that values autonomy and impact.. . Company Location: Armenia.