We are sharing a specialised remote consulting opportunity for experienced Computational Chemistry, Structural Biology, and Drug Discovery Scientists with doctoral-level qualifications and advanced expertise in molecular modelling, protein structure analysis, cheminformatics, and computational drug discovery to contribute their scientific knowledge to advanced AI training and evaluation initiatives.
Selected professionals will design challenging scientific problems, develop rigorous reference solutions, and evaluate AI-generated research outputs across computational chemistry, structural biology, and pharmaceutical research. The work combines advanced molecular simulation, structure-based drug discovery, quantitative modelling, and scientific literature analysis to improve the accuracy, depth, and reliability of AI-generated scientific reasoning.
Key Responsibilities
Computational Chemistry & Scientific Problem Development
- Design technically rigorous, expert-level scientific problems involving computational chemistry, molecular modelling, and drug discovery
- Develop challenging research scenarios requiring sophisticated scientific reasoning and quantitative analysis
- Apply advanced chemical and biophysical principles to evaluate molecular interactions, structural properties, and computational predictions
- Create clearly defined scientific questions with accurate, independently verifiable solutions
- Translate complex computational research concepts into structured AI training and benchmarking tasks
Molecular Modelling & Structural Biology
- Evaluate molecular dynamics simulations, molecular mechanics approaches, and computational studies of biomolecular systems
- Assess simulation methodologies involving GROMACS, AMBER, NAMD, OpenMM, or comparable molecular modelling software
- Analyse molecular structures, protein conformations, ligand interactions, and relevant biophysical properties
- Review protein structure prediction and design workflows using approaches such as AlphaFold, Rosetta, and homology modelling
- Evaluate structural interpretations involving cryo-electron microscopy, X-ray crystallography, and related experimental data
Drug Discovery & Cheminformatics Analysis
- Develop and assess scientific problems involving virtual screening, molecular docking, and structure-based drug discovery
- Evaluate computational approaches using AutoDock, Glide, Schrödinger Suite, MOE, or equivalent software
- Apply cheminformatics principles to molecular representation, chemical fingerprinting, compound libraries, and molecular property analysis
- Review quantitative structure–activity relationships (QSAR), quantitative structure–property relationships (QSPR), and ADMET prediction methodologies
- Analyse computational research findings to identify scientifically meaningful interpretations and technically justified recommendations
AI Training & Scientific Benchmark Evaluation
- Design and critique advanced scientific questions, computational workflows, and technical reasoning chains for AI benchmarking
- Review AI-generated scientific outputs for accuracy, methodological rigour, and depth of reasoning
- Identify subtle errors involving molecular simulations, structural interpretations, chemical assumptions, and quantitative predictions
- Develop detailed evaluation rubrics and reference answers grounded in primary scientific literature and established research methodologies
- Provide structured expert feedback to improve AI performance across computational chemistry, structural biology, and drug discovery applications
Scientific Validation & Research Documentation
- Validate computational analyses, simulation outputs, and research conclusions against established scientific methodologies
- Evaluate the reproducibility, technical consistency, and scientific validity of molecular modelling and computational workflows
- Review proteomics and metabolomics analyses, including relevant mass spectrometry and quantitative data interpretation
- Produce clear technical explanations documenting scientific reasoning, computational methods, and evaluation findings
- Incorporate iterative feedback and peer review to refine scientific problems, reference solutions, and assessment quality
Ideal Profile
- PhD in Computational Chemistry, Structural Biology, Biophysics, Bioinformatics, Medicinal Chemistry, Biochemistry, or a closely related scientific discipline
- Hands-on research experience in an academic or industry research laboratory
- Professional written and spoken English proficiency at C1 level or higher
- Deep expertise in computational chemistry, structural biology, molecular modelling, or computational drug discovery
- Strong understanding of molecular interactions, protein structure, chemical principles, and biophysical research methodologies
- Experience developing or evaluating molecular dynamics simulations using GROMACS, AMBER, NAMD, OpenMM, or comparable tools
- Familiarity with advanced free energy calculation methods, including free energy perturbation (FEP) or thermodynamic integration (TI), is advantageous
- Experience with protein structure prediction, structural analysis, or protein design methodologies
- Knowledge of AlphaFold, Rosetta, homology modelling, cryo-EM, or X-ray crystallography data interpretation is highly relevant
- Practical experience with molecular docking, virtual screening, or structure-based drug discovery workflows
- Familiarity with AutoDock, Glide, Schrödinger Suite, MOE, or equivalent computational drug discovery applications
- Experience in cheminformatics, molecular fingerprinting, compound library analysis, QSAR/QSPR modelling, or ADMET prediction
- Familiarity with RDKit or comparable cheminformatics software
- Knowledge of proteomics or metabolomics workflows involving LC-MS/MS, MaxQuant, DIA/DDA methodologies, post-translational modification analysis, or untargeted metabolomics is beneficial
- Practical proficiency in Python for scientific programming and computational research
- Familiarity with R, Bash, or other relevant computational research tools is advantageous
- Experience applying machine learning to molecular property prediction, including graph neural networks or generative models, is beneficial
- Strong quantitative reasoning, statistical analysis, and scientific problem-solving capabilities
- Postdoctoral or pharmaceutical/biotechnology industry research experience in drug discovery or structural biology is strongly preferred
- Peer-reviewed publications, particularly first-author papers in relevant scientific journals, are highly valued
- Experience with scientific benchmarking, AI model evaluation, or technical research writing and reviewing is advantageous
- Excellent written communication skills, including the ability to explain complex scientific concepts and computational methods precisely
- Strong attention to detail and commitment to scientifically rigorous, reproducible, and well-documented research
Engagement Details
- Freelance / contract engagement
- Fully remote — open worldwide
- Compensation: $45–$90/hour
- 50 available openings
- Specialised scientific consulting role spanning computational chemistry, structural biology, molecular simulation, drug discovery, and AI evaluation
- Academic Requirement: Completed PhD in a relevant scientific discipline
- Research Requirement: Hands-on research experience in an academic or industry laboratory
- Language Requirement: Professional English proficiency at C1 level or higher
- Work will involve expert-level scientific problem development, computational research evaluation, molecular modelling assessment, and AI-generated solution review
- Relevant research areas include molecular dynamics, protein structure prediction, virtual screening, cheminformatics, QSAR/ADMET modelling, proteomics, and metabolomics
- Assignments will require scientifically rigorous reference solutions, literature-informed evaluation rubrics, and detailed technical explanations
- Research experience in pharmaceutical development, biotechnology, structural biology, or computational drug discovery is particularly relevant
- Professionals will contribute to scientific quality assessment through structured feedback, documentation, and iterative peer review
- Collaboration may involve computational scientists, structural biologists, drug discovery researchers, and multidisciplinary technical contributors
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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