Material Science Expert

Prointegrate World It Consulting; United States; Remote; USD 110,000 - 179,000; Part time

Job summary

Prointegrate World It Consulting is seeking a Materials Science Expert to support an AI training project focused on computational materials science. The role emphasizes Python-based automation, materials modeling, and scientific simulation across multiple scales, with flexible scheduling and a remote, international setup.

Immediate start, ~15 hours per week, and an output-based compensation model. This contractor position requires a strong research background and the ability to evaluate modeling

Qualifications

• MS/PhD in Materials Science or related field with strong structure–property knowledge.
• Experience in computational materials modeling and Python.
• Ability to evaluate modeling assumptions and convergence criteria.

Responsibilities

• Solve and validate computational materials-science problems.
• Create material structures, atomic configurations, and inputs.
• Run simulations across atomistic to continuum scales.
• Use Python to automate calculations and analyze results.
• Review AI-generated solutions for scientific accuracy.

Skills

Python

Materials science

Computational modeling

CLI / scripting tools

Education

MS/PhD in Materials Science & Engineering

Tools

LAMMPS

ASE

pymatgen

Quantum ESPRESSO

FEniCSx

CalculiX

Elmer

PyBaMM

Job description

NOW HIRING MATERIALS SCIENCE EXPERT

AI TRAINING | COMPUTATIONAL MATERIALS SCIENCE

Pay: $80$130/hour
Location: Global | 100% Remote
Job Type: Contractor | ~15 hours/week
Schedule: Flexible choose your own hours & days
Start: Immediate

We’re looking for highly skilled Materials Science Experts to contribute to an AI training project involving computational materials science, materials modeling, scientific simulation, and Python.

What You’ll Work On

• Solve and validate computational materials-science problems
  Create material structures, atomic configurations, compositions & simulation inputs
  Run atomistic, molecular dynamics, electronic-structure, continuum, electrochemical or related simulations
  Use Python to automate calculations, generate inputs, analyze results and validate outputs
  Analyze mechanical, thermal, electrical, chemical, structural and electrochemical properties
  Diagnose convergence issues, failed calculations, invalid structures and numerical instability
  Compare computational results with experimental data and literature
  Review AI-generated solutions for scientific accuracy
  Develop reproducible reference solutions and verification methods

Required Qualifications

• MS or PhD in Materials Science & Engineering, Metallurgy, or related field
  OR MS/PhD in Mechanical or Chemical Engineering with substantial materials specialization
  Strong understanding of materials behavior and structure–property relationships
  Experience in computational materials modeling, simulation, characterization or engineering analysis
  Strong Python skills
  Experience with at least one CLI-, scripting-, configuration- or API-based scientific/engineering tool
  Ability to evaluate modeling assumptions, parameters and convergence criteria
  Ability to distinguish computational errors from genuine physical behavior

Relevant Tools

Experience with tools such as LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or equivalent programmatic simulation software.

Python experience with NumPy, SciPy, pandas, Matplotlib, Jupyter, materials informatics libraries or similar tools is valuable.

Experience can come from academic research, national laboratories, industry R&D, computational engineering, or demonstrated materials-science work.

Selection Process

1 Apply & complete screening questions
2 ~30-minute AI interview
3 Technical assessment, if required
4 Hiring manager review
5 Onboarding & project start

Compensation is output-based:

Experts are paid per task that meets project specifications. Task completion time may vary based on experience and workflow.

Fast Hiring:

Roles are typically filled within 48 hours. Selected experts should be ready to begin their first task within 24–48 hours of onboarding.