Materials Science Expert for AI Training

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. OpenTrain AI is hiring and contracting for this role, connecting qualified experts with hands-on projects that help improve how AI systems reason about technical subjects.

Creating an OpenTrain account is free, and selected contractors can apply their professional knowledge to cutting-edge AI work from anywhere.

About AI Training Work

AI training is the human side of building artificial intelligence. Experts create, review, and validate examples that help models produce more accurate, reliable, and scientifically meaningful results.

In this project, your materials science judgment will help assess computational reasoning, simulation workflows, physical assumptions, and technical conclusions.

• Fully remote work from anywhere
• Flexible scheduling, including the option to work weekends
• Part-time commitment of less than 20 hours per week
• Directly contribute to the development of advanced AI systems

The Materials Science Expert Role

OpenTrain is seeking a highly skilled Materials Science Expert for an AI training project focused on computational materials science, materials modeling, scientific simulation, and Python. The work is expected to last under one month, and selected experts should be ready to begin their first task within 24–48 hours of completing onboarding.

You will create, solve, review, and validate engineering tasks involving material structures, properties, processing, performance, and failure. A typical task may involve building a material or atomic model, configuring and running a simulation, calculating properties, analyzing outputs, and determining whether the result is computationally valid and physically meaningful.

• Contractor and part-time engagement
• Global and fully remote
• Expected commitment of less than 20 hours per week
• English-language work requiring fluent English proficiency
• Intermediate experience level

What You’ll Work On

Solutions must be reproducible through code, scripts, configuration files, or command-line tools. Experience limited exclusively to graphical user interfaces will not be sufficient for this role.

• Solve and validate computational materials science and materials engineering problems.
• Create material structures, atomic configurations, compositions, and solver-ready inputs.
• Model relationships among 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 bring strong materials expertise, practical computational experience, and the ability to explain complex scientific reasoning clearly. Experience may come from academic research, national laboratories, industry R&D, computational engineering, or other demonstrated materials work.

• MS or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline
• Alternatively, an MS or PhD in Mechanical Engineering or Chemical Engineering with a substantial materials specialization
• Strong understanding of materials behavior and 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 accessible through a CLI, scripting interface, configuration files, or programmatic API
• Ability to justify modeling assumptions, parameters, approximations, and convergence criteria
• Ability to distinguish computational failures from genuine physical behavior
• Ability to explain technical limitations and complex scientific reasoning clearly

Relevant Tools and Technical Experience

Experience with an equivalent programmatic or CLI-accessible tool is acceptable. No single library is mandatory.

• LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, or PyBaMM
• NumPy, SciPy, pandas, Matplotlib, or Jupyter
• Atomistic modeling packages, materials informatics libraries, or domain-specific scientific tools
• Engineering and scientific software operated through scripts, configuration files, APIs, or command-line tools

Compensation and Availability

The advertised compensation range is $80–$130 per hour. Compensation is output-based, with experts paid per task that meets project specifications. The time required for each task may vary based on experience and workflow, and minimum submission requirements apply.

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

• Advertised rate: $80–$130 per hour
• Output-based payment per approved task
• Flexible schedule under 20 hours per week
• Expected project duration under one month
• Immediate-start availability preferred

Application and Screening Process

The process is designed to assess your materials expertise, technical judgment, and ability to produce reproducible solutions. The technical screening evaluates your personal skills and knowledge.

• 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.
• Create and use a new email address when applying online.
• Do not use AI tools during the technical screening assessment; AI assistance will lead to immediate rejection.