Computational Chemistry and Drug Discovery Scientist (PhD)

Role Overview

Apply advanced scientific expertise to help train next-generation AI systems using rigorous, real-world work in computational chemistry, structural biology, and drug discovery. You will create and evaluate expert-level scientific materials that strengthen model reasoning, accuracy, and depth.

Key Responsibilities

• Design challenging scientific problems in computational chemistry, structural biology, and drug discovery, with clear, verifiable solutions.
• Assess AI model outputs for scientific accuracy, rigor, and reasoning quality, including subtle errors that may be missed by non-specialists.
• Create detailed scoring rubrics and reference answers based on primary literature and established methodologies.
• Apply expertise in molecular modeling and simulation, protein structure prediction and design, virtual screening and docking, QSAR/ADMET modeling, and proteomics or metabolomics data analysis.
• Document reasoning clearly so other experts can review and validate the work.
• Meet quality standards through iterative feedback and peer-review cycles.

Qualifications

• PhD in Computational Chemistry, Structural Biology, Biophysics, Bioinformatics, Medicinal Chemistry, Biochemistry, or a closely related field.
• Hands-on academic or industry laboratory research experience. Wet-lab experience is a plus.
• Professional written and spoken English at C1 level or higher.
• Postdoctoral or pharmaceutical or biotechnology industry research experience in drug discovery or structural biology is preferred.
• A strong publication record, particularly first-author papers in relevant journals, is preferred.
• Experience with molecular modeling and simulation tools and methods, including GROMACS, AMBER, NAMD, OpenMM, FEP, or TI.
• Experience with AlphaFold, Rosetta, homology modeling, or interpretation of cryo-EM or X-ray crystallography data.
• Experience with docking and virtual screening tools, including AutoDock, Glide, Schrödinger Suite, or MOE.
• Cheminformatics experience, including RDKit, QSAR/QSPR, ADMET prediction, fingerprinting, or library design.
• Proteomics or metabolomics experience, including LC-MS/MS, MaxQuant, DIA/DDA workflows, PTM analysis, or untargeted metabolomics.
• Python proficiency is required in practice. R, Bash, similar languages, and machine-learning experience for molecular property prediction, including GNNs or generative models, are beneficial.
• Experience with benchmarking, model evaluation, scientific writing, or scientific reviewing.
• Ability to explain complex scientific concepts precisely and clearly in writing.

Work Terms

• Independent contractor engagement.
• Remote work is available worldwide.

Compensation

$45 to $90 per hour.

Application

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