Computational Theoretical Chemist II

Qualifications

• Ph.D. in computational chemistry or related discipline

• 2 years of relevant industry experience within drug discovery or biotechnology

• Played a key role in advancing a drug discovery program from early research phases to clinical development

• In-depth knowledge and hands-on experience with quantum chemical (QC) methods, including semi-empirical and density functional theory (DFT) approaches, molecular dynamics (MD) simulations, including both standard MD and enhanced sampling techniques such as metadynamics, umbrella sampling, and replica exchange MD, free energy simulations such as FEP and TI, and QM/MM methodologies for small and large molecular systems

• Strong understanding of key concepts, including potential energy surfaces (PES), intermolecular and intramolecular forces/interactions, force fields, molecular properties, thermodynamic properties, solvation models (implicit/explicit), and conformational sampling

• Proficiency in analyzing molecular properties such as solvation free energy, dipole moments, vibrational frequencies, electrostatic potential, charge distribution, and more. - Deep knowledge of implicit and explicit solvent models, with extensive experience modeling solvent effects on molecular systems and chemical reactions in various environments

• Extensive experience in using and troubleshooting software tools for QC calculations (e.g., ORCA, xTB, CREST, etc.), MD simulations (e.g., GROMACS, OpenMM, etc.), Drug Design Development Packages (e.g., EG, Schrodinger, MOE, CRESSET)

• Experience working with HPC Clusters and cloud-based services like (e.g., Microsoft AZURE, AWS)

• Ability to optimize computational simulation protocols for efficient resource usage

• Proven experience working with small organic molecules and large biomolecular systems (e.g., peptides, proteins, etc.) for property prediction, conformational analysis, and structure-activity relationships (SAR)

• Hands-on experience with Python and Bash scripting for automating workflows and data analysis

• Familiarity with cheminformatics toolkits such as RDKit for molecular property prediction and data management

• Basic knowledge of machine learning (ML) techniques applied to molecular property prediction, virtual screening, and related tasks

• Strong desire to collaborate with AI scientists, data scientists, medicinal chemists, and biologists to interpret computational results and guide experimental design

• Clear and effective communication of complex scientific ideas through reports, presentations, and publications

Nice to Haves

• Publications in computational chemistry related to drug discovery

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Diversity and Inclusion (1910’s Promise)

At 1910, we believe that a diverse, equitable, and inclusive workplace furthers relevance, resilience, and longevity. We encourage people from all backgrounds, ages, abilities, and experiences to apply. 1910 is proud to be an equal-opportunity workplace and is an affirmative action employer. If, due to a disability, you need an accommodation during any part of the interview process, please let your recruiter know. While 1910 supports visa sponsorship, sponsorship opportunities may be limited to certain roles and skills. Benefits and Perks

• Competitive compensation package
• Above market benefits
• Generous vacation and parental leave
• Super cool team building activities
• Great colleagues