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

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.