Scientist/Senior Scientist, Structure-Based Modeling

Summary

Join Deep Origin as a Scientist or Senior Scientist and contribute to a transformative ARPA-H initiative focused on structure-based drug design. Lead the design of robust simulation workflows and analyze protein-ligand structures to support predictive modeling for therapeutic discovery. You will analyze protein targets, run and refine simulations using advanced tools, apply various methods to ensure robust workflows, curate and select optimal structures, evaluate multiple representations, and collaborate with cross-functional teams. Communicate progress and findings effectively to internal and external stakeholders. Deep Origin offers a remote-first team, competitive salary and equity, flexible hours, and a mission-driven culture.

Requirements

• 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

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

Preferred Qualifications

• 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

Benefits

• 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