Computational Chemistry, Structural Biology & Drug Discovery Scientist (PhD)

Role Title: Computational Chemistry, Structural Biology & Drug Discovery Scientist (PhD) Role Type: Contractor Location: Remote (Worldwide) Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. Key Responsibilities - Design and write challenging, expert-level scientific problems in computational chemistry, structural biology, and drug discovery, with clear, verifiable solutions. - Review and evaluate AI model outputs for scientific accuracy, rigor, and depth of reasoning, identifying subtle errors that non-specialists would miss. - Develop detailed rubrics and reference answers grounded in the primary literature and established methodology. - Apply domain expertise across areas such as molecular modeling and simulation, protein structure prediction and design, virtual screening and docking, QSAR/ADMET modeling, and proteomics/metabolomics data analysis. - Provide clear written explanations of your reasoning, so that other experts can follow and validate it. - Meet quality standards through iterative feedback and peer review cycles. Required Qualifications (MUST) - PhD in Computational Chemistry, Structural Biology, Biophysics, Bioinformatics, Medicinal Chemistry, Biochemistry, or a closely related field. - Hands-on research experience in an academic or industry lab (wetlab experience is a plus). - Professional written and spoken English (C1 or higher). Preferred Skills - Postdoctoral or industry research experience (pharma/biotech) in drug discovery or structural biology. - Strong publication record, particularly first-author papers in relevant journals. - Molecular modeling and simulation: GROMACS, AMBER, NAMD, OpenMM, free energy methods (FEP/TI). - Structure prediction and design: AlphaFold, Rosetta, homology modeling, cryo-EM / X-ray crystallography data interpretation. - Docking and virtual screening: AutoDock, Glide, Schrödinger Suite, MOE. - Cheminformatics: RDKit, QSAR/QSPR, ADMET prediction, fingerprinting, library design. - Proteomics and metabolomics: LC-MS/MS, MaxQuant, DIA/DDA workflows, PTM analysis, untargeted metabolomics. - Programming: Python (required in practice), plus R, Bash, or similar; experience with ML for molecular property prediction (GNNs, generative models) is a plus. - Experience with benchmarking, model evaluation, or scientific writing/reviewing. - Ability to explain complex scientific concepts clearly and precisely in writing.