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

About the role

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.

Scope of Work

• 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.

What you’ll bring

• 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 Qualifications

• 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.

Benefits

Not provided in the source listing.

Contract & Payment Terms

• Source posting label: Today, captured 16 October 2026; approximate employer posting date 16 October 2026. The exact source timestamp and timezone were not supplied. Remolity publication is tracked separately.

Where you can work

The listing states worldwide eligibility. Confirm work authorization with the employer.

Remote does not always mean work from any country. Always check the employer’s location and working-hour requirements.