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

Role Type: Contractor
Location: Remote (Worldwide)

About the Role

We are seeking PhD-level experts in Computational Chemistry, Structural Biology, and Drug Discovery to contribute their scientific expertise to the development and evaluation of next-generation AI systems.

In this role, you will create challenging scientific problems, evaluate AI-generated outputs, and assess the accuracy, rigor, and depth of scientific reasoning. Your expertise will help ensure that AI systems deliver reliable, evidence-based insights across molecular modeling, protein structure analysis, and drug discovery.

Key Responsibilities

1. Design and develop challenging, expert-level scientific problems in computational chemistry, structural biology, and drug discovery, with clear and verifiable solutions.

2. Review and evaluate AI-generated outputs for scientific accuracy, methodological rigor, and depth of reasoning.

3. Identify subtle scientific errors, unsupported conclusions, and methodological limitations.

4. Develop detailed evaluation rubrics and reference answers grounded in primary literature and established scientific methodologies.

5. 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 and metabolomics data analysis.

6. Provide clear, detailed written explanations of scientific reasoning to support expert review and validation.

7. Maintain quality standards through iterative feedback, revisions, and peer review.

Required Qualifications

• 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 laboratory. Wet lab experience is an advantage.

• Professional written and spoken English proficiency (C1 level or higher).

Preferred Skills and Qualifications

• Postdoctoral or industry research experience in drug discovery or structural biology, preferably within the pharmaceutical or biotechnology sector.

• Strong publication record, particularly first-author publications in relevant scientific journals.

• Experience with molecular modeling and simulation tools such as GROMACS, AMBER, NAMD, OpenMM, and free energy methods (FEP/TI).

• Familiarity with structure prediction and design tools, including AlphaFold, Rosetta, homology modeling, and interpretation of cryo-EM or X-ray crystallography data.

• Experience with docking and virtual screening platforms such as AutoDock, Glide, Schrödinger Suite, or MOE.

• Knowledge of cheminformatics methods and tools, including RDKit, QSAR/QSPR, ADMET prediction, molecular fingerprinting, and library design.

• Experience with proteomics and metabolomics techniques, including LC-MS/MS, MaxQuant, DIA/DDA workflows, PTM analysis, and untargeted metabolomics.

• Proficiency in Python, with additional experience in R, Bash, or similar programming tools.

• Familiarity with machine learning methods for molecular property prediction, including graph neural networks (GNNs) and generative models.

• Experience with scientific benchmarking, AI model evaluation, or scientific writing and reviewing.

• Ability to explain complex scientific concepts clearly and precisely in writing.