Remote | Materials Science Expert — $80–$130/hour
About The Position
24-MAG LLC is recruiting experienced materials scientists and engineers for a specialised, part-time consulting engagement supporting an advanced AI training and engineering evaluation project. Professionals will create, solve, review, and validate technically rigorous materials-science problems involving material structures, properties, processing, performance, and failure. The work combines domain expertise, computational modelling, Python-based automation, numerical validation, and critical review of engineering solutions. Strong experience with programmatic scientific or engineering workflows is essential. The engagement is fully remote and open to candidates worldwide, with an expected commitment of approximately 15 hours per week and a flexible schedule, including evenings or weekends. Compensation is $80–$130 per hour and is output-based, with payment for tasks that meet project specifications. Minimum submission requirements apply. Selected professionals should be prepared to begin their first task within approximately 24–48 hours of completing onboarding. Roles are typically filled within approximately 48 hours. The selection process may include screening questions, an approximately 30-minute AI interview, and hiring-manager review. Work must not use confidential or proprietary information belonging to an employer, client, institution, research group, or other third party.
Requirements
- Master’s or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline; alternatively, a Master’s or PhD in Mechanical Engineering or Chemical Engineering with substantial materials specialisation.
- Strong understanding of materials behaviour and structure-property relationships.
- Experience with computational materials modelling, simulation, characterisation, or materials-focused engineering analysis.
- Practical proficiency with Python.
- Experience with at least one scientific or engineering tool operable through a CLI, scripting interface, configuration files, or programmatic API.
- Ability to justify modelling assumptions, simulation parameters, approximations, and convergence criteria.
- Ability to distinguish computational failures from valid physical phenomena.
- Strong technical communication skills and ability to explain complex scientific reasoning clearly.
- Experience limited exclusively to graphical-user-interface workflows is not sufficient.
- Must complete work without using confidential or proprietary third-party information.
Nice To Haves
- Experience may come from academic research, national laboratories, industry R&D, computational engineering, or comparable materials-focused work.
- Familiarity with tools such as LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or comparable software.
- Experience with scientific Python tools such as NumPy, SciPy, pandas, Matplotlib, Jupyter, or comparable libraries.
Responsibilities
- 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.
- Apply appropriate scientific assumptions and modelling approaches to complex materials problems.
- Evaluate whether computational results are physically meaningful and consistent with known material behaviour.
- Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.
- Analyse mechanical, thermal, electrical, chemical, structural, and electrochemical properties.
- Select and justify simulation parameters, approximations, and convergence criteria.
- Diagnose failed calculations, invalid structures, numerical instability, and convergence problems.
- Distinguish genuine physical behaviour from computational artefacts or modelling failures.
- Use Python to generate simulation inputs and automate computational workflows.
- Conduct parameter sweeps, post-process simulation results, and validate outputs.
- Build reproducible workflows using scripts, configuration files, command-line tools, or programmatic APIs.
- Compare computational results with experimental data, literature values, known material properties, or expected physical trends.
- Review AI-generated materials-science solutions for scientific correctness.
- Identify invalid assumptions, configurations, calculations, or conclusions.
- Develop reproducible reference solutions and objective verification criteria.
- Clearly document technical reasoning, limitations, and validation methodology.
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What This Job Offers
Job Type
Part-time
Career Level
Senior
