About this opportunity
Confidential lists this Computational Chemistry, Structural Biology & Drug Discovery Scientist (PhD) opportunity in Cheltenham, Maryland. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Role Overview
Apply advanced expertise in computational chemistry, structural biology, and drug discovery to help train and evaluate next-generation AI systems. You will create rigorous scientific materials that improve how models reason and perform in specialized life-science domains.
Key Responsibilities
Design expert-level problems in computational chemistry, structural biology, and drug discovery, with clear, verifiable solutions.
Review AI-generated outputs for scientific accuracy, rigor, and depth of reasoning, including subtle errors that may be missed by non-specialists.
Create detailed scoring rubrics and reference answers based on primary literature and established methodologies.
Apply expertise in molecular modeling and simulation, protein structure prediction and design, virtual screening and docking, QSAR and ADMET modeling, and proteomics or metabolomics data analysis.
Document reasoning clearly so other experts can review and validate it.
Meet quality standards through iterative feedback and peer-review cycles.
Qualifications
PhD in Computational Chemistry, Structural Biology, Biophysics, Bioinformatics, Medicinal Chemistry, Biochemistry, or a closely related field.
Hands-on academic or industry laboratory research experience. Wet-lab experience is an advantage.
Professional written and spoken English at C1 level or higher.
Ability to explain complex scientific concepts clearly and precisely in writing.
Preferred Expertise
Postdoctoral or pharmaceutical or biotechnology industry research experience in drug discovery or structural biology.
A strong publication record, particularly first-author papers in relevant journals.
Molecular modeling and simulation experience with GROMACS, AMBER, NAMD, OpenMM, or free-energy methods such as FEP and TI.
Experience with AlphaFold, Rosetta, homology modeling, or cryo-EM and X-ray crystallography data interpretation.
Experience with AutoDock, Glide, Schrödinger Suite, or MOE for docking and virtual screening.
Cheminformatics experience with RDKit, QSAR or QSPR, ADMET prediction, fingerprinting, or library design.
Proteomics or metabolomics experience with LC-MS/MS, MaxQuant, DIA or DDA workflows, PTM analysis, or untargeted metabolomics.
Python proficiency, with R, Bash, or similar tools also valued. Experience applying machine learning to molecular property prediction, including GNNs or generative models, is a plus.
Experience with benchmarking, model evaluation, or scientific writing and review.
Work Terms
Remote contract engagement open worldwide.
Compensation
Hourly pay of $45 to $90.
Application Process
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.