Computational Chemistry Expert for AI Benchmarking

About OpenTrain

OpenTrain is the #1 platform for people who build careers in AI training and data labeling. We connect subject-matter experts with projects that teach AI systems how to reason, evaluate, and produce reliable results.

We focus on remote, flexible work that helps contributors turn domain expertise into durable freelance careers—tracking projects, building a portfolio, and applying quickly to relevant roles.

Why AI training in chemistry matters

Modern chemistry and drug-discovery tools increasingly rely on machine-learned models that were taught from human-reviewed examples. Expert evaluation of simulation workflows, electronic-structure methods, and software configurations is essential to ensure those models learn from accurate, reproducible science.

This role places you on the cutting edge: your judgments and documentation help train, benchmark, and validate chemistry-focused AI tools used by researchers and practitioners.

The role

We are recruiting a computational chemistry expert to review and critique workflows, evaluate outputs, and document recommendations used in AI benchmarking. You will provide rigorous, reproducible feedback on simulations, quantum chemistry, and cheminformatics pipelines so AI systems learn from high-quality scientific examples.

This is a remote, contract, part-time role requiring a minimum of 20 hours per week. Work is paid hourly at $40–$60/hour (typical rate offered up to $60/hr).

• Employment type: Contractor, Part-time
• Time requirement: 20+ hours/week
• Pay: $40–$60 per hour (hourly rate listed: $60/hr)
• Language: English

What you'll do

Your core responsibilities are focused on technical evaluation, clear documentation, and collaboration with technical and non-technical reviewers who rely on your chemistry judgment.

Expect to work with document-based evaluations and provide ratings and written feedback that improve model training and benchmarking.

• Review and critique computational chemistry workflows used for AI benchmarking
• Evaluate molecular simulations, electronic-structure methods, and cheminformatics pipelines
• Assess simulation parameters and software configurations for accuracy and reproducibility
• Analyze outputs from molecular dynamics, quantum chemistry, and drug-discovery investigations
• Provide expert feedback that supports development and validation of chemistry-related AI models and tools
• Document methodologies, findings, and best practices for clear knowledge transfer
• Communicate technical recommendations to both technical and non-technical collaborators

Requirements

You must bring deep, demonstrable experience in computational chemistry and strong scientific programming skills. Candidates should be able to evaluate methods and interpret quantitative outputs with high scientific rigor.

• PhD in chemistry, computational chemistry, or a closely related field — or equivalent industry/research experience
• Deep experience with computational chemistry methods and simulation-heavy environments
• Strong scientific programming skills, especially in Python
• Familiarity with tools such as Gaussian, ORCA, Psi4, NWChem, GROMACS, LAMMPS, AMBER, RDKit, or OpenBabel
• Excellent quantitative, analytical, and scientific reasoning skills
• Strong written and verbal communication for documentation and collaboration
• Track record of rigorous scientific problem-solving in complex chemistry domains

Helpful background and signals of success

The following are not required but will make an application stand out and help you be effective faster on this work.

• Experience with AI or ML-assisted chemistry workflows or benchmarking
• Peer-reviewed publications, patents, or open-source scientific software contributions
• Background in drug discovery, spectroscopy analysis, reaction prediction, or retrosynthesis

How it works — application and collaboration

Apply through OpenTrain to join this contract project. If selected you'll complete task-based evaluations and submit ratings plus concise technical write-ups. Work products are used to train and benchmark AI models and to build reproducible best-practice guidance.

Expect asynchronous work with clear instruction materials and examples; you will be asked to follow defined evaluation rubrics and deliver high-quality documented findings.

• Data type: Document-based evaluations; label type: evaluation/rating
• Worldwide applicants accepted; English required
• Contract starts after selection and onboarding via OpenTrain