Apply advanced atomistic and surface-modeling expertise to generate, structure, and evaluate training data for frontier AI systems in materials science and physical sciences. Contract remotely for 20+ hours per week at $84 per hour.
Generative AI & RLHF
Remote Hourly · $84/hr
$84/hr
Compensation
1 country
Eligibility
Entry
Experience
Aug 7, 2026
Posted
Open to applicants in
United States
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About AI Training in Materials Science
AI training is the human side of building advanced artificial intelligence. Specialists prepare and review examples, evaluate model outputs, and provide the technical judgment models need to reason more accurately.
In this project, your expertise will help shape AI systems working with materials, surfaces, chemical processes, and physical-science problems. It is an opportunity to contribute directly to how frontier models learn specialized scientific reasoning.
The Role
OpenTrain AI is seeking an Atomistic and Surface Modeling Expert to support scientific AI training and evaluation. You will apply deep knowledge of first-principles and molecular simulation to create high-quality data and assess whether AI-generated scientific reasoning is technically sound.
This is a part-time contractor role for contributors in the United States, requiring 20 or more hours per week. The rate is $84 per hour.
Contractor and part-time engagement
United States-based
20+ hours per week
$84 per hour
Primary language: English
What You'll Do
You will work on expert-level scientific content involving atomistic modeling, surfaces, interfaces, adsorption, reactions, and related simulation methods. Your work will be structured for use in AI training and evaluation.
Contribute domain expertise across first-principles and molecular simulation.
Generate high-quality training and evaluation data for materials-science AI systems.
Review AI-generated scientific reasoning and identify technical errors.
Improve the accuracy and clarity of model-generated scientific responses.
Design and solve challenging problems in atomistic and surface modeling.
Rate and rank model outputs against defined scientific criteria.
Explain evaluations and rankings with clear written reasoning.
Structure simulation setups, methods, and results into organized, model-ready data.
Deliver reliable, high-quality work within defined timelines.
Required Qualifications
This role requires specialized academic and research expertise in atomistic or surface modeling. The listed experience level is entry level, but the technical requirements include doctoral training and hands-on experience with advanced simulation methods.
PhD in materials science, chemistry, physics, chemical engineering, or a related field.
Hands-on experience with first-principles or molecular simulation methods, including DFT, ab initio molecular dynamics, classical MD, or Monte Carlo.
Experience modeling surfaces, interfaces, adsorption, or reaction phenomena.
Knowledge of slab models, surface reconstructions, transition states, NEB, or microkinetics.
Experience with semiconductor-relevant materials or computational heterogeneous catalysis.
Proficiency with VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, ASE, or pymatgen.
Clear written English and the ability to explain technical reasoning concisely.
Helpful Background
Experience generating or evaluating data for AI training in materials science is a plus. Familiarity with scientific data quality standards can help you communicate complex technical judgments clearly.
Why Join AI Training Work
AI training offers a flexible way for specialists to contribute to the development of modern AI systems. Remote project work can fit alongside research, teaching, or other professional commitments while putting your subject-matter expertise to use.
Work remotely from the United States.
Contribute to cutting-edge AI systems for materials science and physical sciences.
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