Materials Science Expert

About OpenTrain

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About AI Training Work

AI training is the human side of building modern artificial intelligence. Experts create, review, and evaluate examples that help AI systems improve, including technical reasoning, code, simulations, and scientific problem-solving.

In this project, your materials science expertise will help assess whether AI-generated solutions are computationally reproducible, scientifically correct, and physically meaningful. The work is fully remote and designed for flexible participation.

The Role

OpenTrain AI is seeking an expert in computational materials science and engineering simulation for a fully remote contractor project. You will create, solve, review, and validate technical tasks involving material structures, properties, processing, performance, and failure.

A representative task may involve constructing a material or atomic model, configuring and running a simulation, calculating relevant properties, analyzing outputs, and determining whether the result is computationally valid and physically meaningful.

This role requires both strong materials expertise and practical programming experience. Experience limited exclusively to graphical user interfaces is not sufficient because solutions must be reproducible through code, scripts, configuration files, or command-line tools.

• Pay range: $80-$130 per hour, with an advertised midpoint of $105 per hour
• Fully remote and open globally
• Contractor, part-time engagement
• Expected commitment: less than 20 hours per week
• Expected project duration: 1 to 3 months
• Flexible schedule, including the option to work weekends
• English-language work requiring fluent English proficiency
• Experts should be ready to begin immediately

What You’ll Work On

You will contribute to computer code programming, evaluation rating, text generation, and question answering workflows focused on computational materials science and engineering simulation.

• Solve and validate computational materials science and materials engineering problems.
• Create material structures, atomic configurations, compositions, and solver-ready inputs.
• Model relationships between composition, structure, processing, properties, and performance.
• Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.
• Use Python to generate inputs, automate calculations, conduct parameter sweeps, process results, and validate outputs.
• Analyze mechanical, thermal, electrical, chemical, structural, or electrochemical properties.
• Diagnose failed calculations, invalid structures, convergence problems, numerical instability, and incorrect physical assumptions.
• Compare computational results with experimental data, literature values, known properties, or expected physical trends.
• Review AI-generated solutions for scientific correctness and identify invalid assumptions, configurations, or conclusions.
• Develop reproducible reference solutions and objective verification methods.

Required Qualifications

You should have advanced materials expertise and experience applying computational or engineering methods to materials problems. Experience may come from academic research, national laboratories, industry research and development, computational engineering, or other demonstrated materials work.

• MS or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline; or an MS or PhD in Mechanical Engineering or Chemical Engineering with a substantial materials specialization.
• Strong understanding of materials behavior and relevant structure-property relationships.
• Experience with computational materials modeling, simulation, characterization, or materials-focused engineering analysis.
• Practical proficiency with Python.
• Experience with at least one engineering or scientific tool operable through a command-line interface, scripting interface, configuration files, or programmatic API.
• Ability to understand and justify modeling assumptions, parameters, approximations, and convergence criteria.
• Ability to distinguish computational failures from genuine physical behavior.
• Ability to explain complex scientific reasoning and technical limitations clearly.
• Experience with computer code programming labeling workflows.
• Experience with evaluation rating, text generation, or question answering tasks.

Relevant Tools and Technical Experience

Relevant tools may include LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or similar programmatic materials and simulation software. Experience with an equivalent command-line-accessible tool is acceptable.

Relevant Python tools may include NumPy, SciPy, pandas, Matplotlib, Jupyter, atomistic modeling packages, materials informatics libraries, or domain-specific scientific tools. No single library is mandatory.

Compensation, Availability, and Selection Process

Compensation is output-based: experts are paid per task that meets project specifications, and the time required for each task may vary based on experience and workflow. The role is listed with an $80-$130 per-hour pay range. Minimum submission requirements apply.

OpenTrain AI typically fills roles within 48 hours. If selected, you will be expected to start your first task within 24-48 hours after completing onboarding.

• Apply to the role and complete the screening questions.
• Complete an approximately 30-minute AI interview.
• Complete a technical assessment if required.
• Complete the hiring manager review.
• Begin the first task within 24-48 hours of onboarding if selected.