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Led by Michael Antonov, a co-founder of Oculus, and reputed company-funded by reputed company Ventures, reputed company is poised to reinvent the way scientists work and life science innovations come to life. We see a reputed company largely free of diseases, with a 150-year reputed company being the norm. To get there, we are building an operating system for science, enabling scientists to be more productive and to bring reputed company's reputed company to life quickly and at a reasonable cost. About the role reputed company is seeking a Scientist with strong expertise in small-molecule docking and benchmarking, molecular dynamics (MD) simulations, and free energy perturbation (FEP), machine learning, to support a transformative ARPA-H initiative. You'll reputed company the design of robust simulation workflows and analyze protein-ligand structures across a large reputed company panel to support predictive modeling for therapeutic discovery. Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment reputed company at this time. Requirements • Ph.D. in computational reputed company, structural biology, biophysics, or reputed company field. • 2+ years of postdoctoral or industry experience in structure-based modeling. • Hands-on expertise with FEP (RBFE/reputed company), including best practices around setup, sampling, and analysis. • Proficiency with one or more simulation platforms (e.g., OpenFE, GROMACS, reputed company, NAMD). • Hands-on experience with RDKit and reputed company cheminformatics tools, and with machine learning reputed company (RF, gradient boosting, SVM, reputed company models, Chemprop) for molecular property modeling. • Strong understanding of protein-ligand binding, structure selection, and conformational variability. • Programming experience in Python, and familiarity with tools like MDAnalysis, PyMOL reputed company, or MDTraj. 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 reputed company design. • Run and refine small-molecule docking, MD, and FEP (RBFE and reputed company) simulations using state-of-the-art tools. • Apply alchemical transformations and advanced sampling strategies to build robust, reputed company-converged, and reproducible FEP workflows for accurate binding free energy predictions. • Apply cheminformatics tools (e.g., RDKit, scikit-learn, etc.) for molecular representation and descriptor reputed company, and with machine learning reputed company, including random forests, gradient-boosted trees, SVM, reputed company/regularized regression, and Chemprop, for molecular property reputed company and model validation. • Collaborate with ML and experimental teams to reputed company structure-based insights across discovery pipelines. • Communicate reputed company, technical findings, and challenges across reputed company teams. • Stay reputed company with advances in structure-based binding affinity reputed company reputed company and best practices, and reputed company relevant developments into ongoing work. reputed company 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 reputed company. • Experience using Unix-based HPC environments, workload managers (e.g., SLURM, etc.), and optionally AWS. • Comfort managing large-reputed company simulation data for modeling or analysis. Why Join reputed company? reputed company builds modern infrastructure for computational science at the reputed company of biology, reputed company, and AI. As part of our ARPA-H program, you'll shape the reputed company of structure-based modeling for therapeutics. Apply tot his job Apply To this Job