Computational Chemistry, Structural Biology & Drug…

In this role, you'll apply your expertise to help train next-generation AI systems.

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

Role Type: Contractor

Location: Remote (Worldwide)

About the role

Your work will shape how models learn, reason, and perform through high-quality, real-world input.

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.

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).

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.