Computational Chemistry, Structural Biology & Drug

You will help improve AI systems by creating difficult scientific problems, reviewing model responses, and explaining the reasoning behind your judgments. Your work will focus on computational chemistry, structural biology, drug discovery, and related molecular science.

You will use primary literature and established methods to create reference answers and detailed scoring guides. You will also take part in peer review and feedback cycles so other experts can validate the scientific quality of the work.

• Design challenging, verifiable problems in computational chemistry, structural biology, and drug discovery.
• Review AI-generated answers for scientific accuracy, rigor, depth of reasoning, and subtle technical errors.
• Create rubrics and reference solutions grounded in primary literature and established methodology.
• Apply knowledge of molecular modeling, protein structure prediction, virtual screening, docking, QSAR, ADMET, proteomics, and metabolomics.
• Explain scientific conclusions clearly for expert review and participate in iterative feedback and peer review.

This is remote contractor work with part-time employment status. It is open to candidates in the listed countries, subject to applicable hiring eligibility requirements.

• Pay: $45 to $90 per hour.
• Location: Remote, open in the listed countries.
• Language: Professional written and spoken English at C1 level or higher.
• Education: PhD in computational chemistry, structural biology, biophysics, bioinformatics, medicinal chemistry, biochemistry, or a closely related field.
• Experience: Hands-on research experience in an academic or industry laboratory.
• Skills: Practical Python programming, strong scientific judgment, and precise technical writing.
• Helpful background: Postdoctoral, pharmaceutical, or biotechnology research in drug discovery or structural biology.
• Helpful experience: Publications, especially first-author papers; scientific reviewing; benchmarking; model evaluation; molecular property prediction; graph neural networks; or generative models.
• Relevant tools and methods include GROMACS, AMBER, NAMD, OpenMM, FEP, TI, AlphaFold, Rosetta, AutoDock, Glide, Schroedinger Suite, MOE, RDKit, QSAR, ADMET, and LC-MS/MS analysis.

You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.

The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 3 screening questions.

AI training is the human work behind systems that generate and assess text, images, code, and scientific answers. Specialists are paid for this work because their judgment helps models produce more accurate and reliable results in complex fields.

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