Job Title: Materials Science Expert Job Type: Contractor Location: Remote

About the hiring company:

Our client is a rapidly growing, venture-backed AI company helping shape the next generation of intelligent systems. By combining world-class human expertise with advanced machine learning workflows, they enable leading AI organizations to build, evaluate, and improve cutting-edge models used across a wide range of industries. The company works with highly accomplished professionals in fields such as software engineering, finance, healthcare, legal, operations, research, and other specialized domains. These experts contribute directly to the development of advanced AI systems by providing real-world knowledge, evaluations, feedback, and domain-specific judgment that help models reason more accurately and perform more effectively. Leveraging a proprietary AI-driven talent assessment and matching platform, the organization identifies exceptional professionals globally and connects them with high-impact projects at the forefront of artificial intelligence. Backed by more than $40 million in funding and supported by a rapidly expanding international network of experts, the company is building critical human intelligence infrastructure for the AI economy and creating meaningful opportunities for professionals to apply their expertise in entirely new ways.

Job Summary:

We are looking for a highly skilled Materials Science Expert to contribute to an AI training project involving computational materials science, materials modeling, scientific simulation, and Python. The work involves creating, solving, reviewing, and validating engineering tasks related to material structures, properties, processing, performance, and failure. A representative task may require constructing a material or atomic model, configuring and running a simulation, calculating relevant properties, analyzing the resulting outputs, and determining whether the solution is computationally valid and physically meaningful. This role requires both strong materials expertise and experience using engineering or scientific tools programmatically. Experience limited exclusively to graphical user interfaces will not be sufficient, as task solutions must be reproducible through code, scripts, configuration files, or command-line tools.

What You'll Work On

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

An 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 that can be operated through a CLI, 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.
Relevant tools may include LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM , or similar programmatic materials and simulation software. Experience with an equivalent CLI-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.
Experience may come from academic research, national laboratories, industry R&D, computational engineering, or other demonstrated materials work.

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