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Confidential

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

North Bethesda, MD

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About this opportunity

Confidential lists this Computational Chemistry, Structural Biology & Drug Discovery Scientist (PhD) opportunity in North Bethesda, Maryland. Review the employer’s description below for duties, qualifications and application requirements.

Job description

Apply advanced expertise in computational chemistry, structural biology, and drug discovery to help train and evaluate next-generation AI systems. This remote contract role focuses on creating rigorous scientific content that improves how AI models reason, assess evidence, and solve specialized research problems.

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

Review AI-generated responses 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 scientific methods.

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 the work.

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 a plus.

Professional written and spoken English at C1 level or higher.

Postdoctoral or pharma and biotech industry research experience in drug discovery or structural biology is preferred.

A strong publication record, especially first-author papers in relevant journals, is preferred.

Experience with molecular modeling and simulation tools such as GROMACS, AMBER, NAMD, OpenMM, and free-energy methods including FEP and TI.

Experience with AlphaFold, Rosetta, homology modeling, or interpretation of cryo-EM and X-ray crystallography data.

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

Cheminformatics experience with RDKit, QSAR or QSPR, ADMET prediction, fingerprinting, or library design.

Proteomics or metabolomics experience, including LC-MS/MS, MaxQuant, DIA or DDA workflows, PTM analysis, or untargeted metabolomics.

Practical Python proficiency. Experience with R, Bash, related languages, or machine learning for molecular property prediction, including GNNs and generative models, is a plus.

Experience with benchmarking, model evaluation, scientific writing, or peer review, along with the ability to explain complex concepts precisely in writing.

Work Terms Independent contractor engagement.

Remote work available worldwide.

Compensation Hourly pay of $45 to $90.

Application Submit an application to be considered. By continuing the application, you acknowledge the applicable candidate terms and privacy policy.

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