Computational Chemistry AI Expert

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. Create a free OpenTrain account to discover projects, consolidate opportunities, and build a unified portfolio of AI training work you control.

About AI training work

AI training (data labeling and human feedback) is the human side of building modern AI: people prepare, review, and evaluate the examples AI models learn from. This work is highly flexible, often remote, and lets you directly shape how cutting-edge systems behave.

Many projects need domain experts to ensure scientific accuracy — exactly the role this position fills for chemistry-focused AI benchmarking and validation.

The role

OpenTrain is recruiting a Computational Chemistry AI Expert to support benchmarking and model validation for chemistry-related AI systems. This is a contractor, part-time role focused on review and critique of computational workflows, simulations, and AI-assisted chemistry outputs.

• Time commitment: 20+ hours per week (part-time contractor)
• Pay: hourly, range $20–$60/hour (posted rate up to $60/hr)
• Language: English required
• Work location: Remote; worldwide applicants accepted
• Data type: document review; label type: evaluation/rating

What you'll do

Use your computational chemistry expertise to evaluate methods, simulations, and the outputs of chemistry-related AI tools. Deliver clear, documented guidance that helps researchers and engineers improve model accuracy and reproducibility.

• Design, evaluate, and critique computational chemistry workflows for AI benchmarking initiatives
• Review molecular simulations, quantum chemistry calculations, molecular dynamics, and drug-discovery outputs
• Assess simulation parameters and scientific software configurations for accuracy and reproducibility
• Guide the development and validation of chemistry-related AI models and tools using domain expertise
• Document methodologies, findings, and best practices for both technical and non-technical collaborators

Requirements & preferred qualifications

You must demonstrate advanced domain knowledge and strong scientific programming skills. The items below are required or strongly preferred as listed.

• Required: PhD in chemistry, computational chemistry, or a closely related field — or equivalent industry/research experience
• Required: Strong scientific programming and analytical workflow experience, especially in Python
• Required: Deep familiarity with computational chemistry methods and simulation-heavy environments
• Required: Clear written and verbal communication skills; able to explain complex scientific concepts
• Preferred: Experience with AI- or ML-assisted chemistry workflows or benchmarking
• Preferred tools (plus): Gaussian, ORCA, Psi4, NWChem, GROMACS, LAMMPS, AMBER, RDKit, OpenBabel
• Helpful: Peer-reviewed publications, patents, or contributions to open-source scientific software
• Helpful: Background in drug discovery, spectroscopy analysis, reaction prediction, or retrosynthesis

Who should apply

Apply if you are an experienced computational chemist or research scientist who enjoys translating technical science into clear evaluations that improve AI systems. Ideal candidates combine deep domain knowledge, hands-on simulation experience, and strong Python-based tooling skills.

• Experienced computational chemists with simulation and electronic-structure expertise
• Researchers comfortable documenting methods and communicating with multi-disciplinary teams
• People who have worked with or evaluated AI/ML tools in chemistry contexts

How it works

Create a free OpenTrain account, complete your profile, and apply to this project. OpenTrain will review applicants and onboard selected contractors with project-specific instructions and access. Work is paid hourly under a contractor agreement; exact onboarding and payment details are provided by OpenTrain if selected.

• Apply through OpenTrain with your CV, relevant publications or code, and examples of prior simulation or benchmarking work
• Selected contributors will receive project briefs and criteria for evaluation/rating tasks
• (project-specific tools); data: document-based evaluation/rating tasks