{"html":"<p><strong>Role Title: </strong>Computational Chemistry, Structural Biology &amp; Drug Discovery Scientist (PhD)</p><p><strong>Role Type: </strong>Contractor</p><p><strong>Location:</strong> Remote (Worldwide)</p><p><strong>Job Summary:</strong> 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.</p><h4>Key Responsibilities</h4><ul><li>Design and write challenging, expert-level scientific problems in computational chemistry, structural biology, and drug discovery, with clear, verifiable solutions.</li><li>Review and evaluate AI model outputs for scientific accuracy, rigor, and depth of reasoning, identifying subtle errors that non-specialists would miss.</li><li>Develop detailed rubrics and reference answers grounded in the primary literature and established methodology.</li><li>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.</li><li>Provide clear written explanations of your reasoning, so that other experts can follow and validate it.</li><li>Meet quality standards through iterative feedback and peer review cycles.</li></ul><h4>Required Qualifications (MUST)</h4><ul><li>PhD in Computational Chemistry, Structural Biology, Biophysics, Bioinformatics, Medicinal Chemistry, Biochemistry, or a closely related field.</li><li>Hands-on research experience in an academic or industry lab (wetlab experience is a plus).</li><li>Professional written and spoken English (C1 or higher).</li></ul><h4>Preferred Skills</h4><ul><li>Postdoctoral or industry research experience (pharma/biotech) in drug discovery or structural biology.</li><li>Strong publication record, particularly first-author papers in relevant journals.</li><li>Molecular modeling and simulation: GROMACS, AMBER, NAMD, OpenMM, free energy methods (FEP/TI).</li><li>Structure prediction and design: AlphaFold, Rosetta, homology modeling, cryo-EM / X-ray crystallography data interpretation.</li><li>Docking and virtual screening: AutoDock, Glide, Schrödinger Suite, MOE.</li><li>Cheminformatics: RDKit, QSAR/QSPR, ADMET prediction, fingerprinting, library design.</li><li>Proteomics and metabolomics: LC-MS/MS, MaxQuant, DIA/DDA workflows, PTM analysis, untargeted metabolomics.</li><li>Programming: Python (required in practice), plus R, Bash, or similar; experience with ML for molecular property prediction (GNNs, generative models) is a plus.</li><li>Experience with benchmarking, model evaluation, or scientific writing/reviewing.</li><li>Ability to explain complex scientific concepts clearly and precisely in writing.</li></ul>"}