Job Description Role Title: Materials Science Expert
Role Type
Contractor
Location
Remote
Our team is selecting Materials Science Experts to contribute to a customer's project focused on advancing the capabilities of AI technologies in materials engineering. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required, your domain knowledge is what matters. This is an opportunity to impact the future of engineering by ensuring that AI systems can accurately interpret, solve, and explain complex materials challenges spanning selection, failure analysis, testing, and process development. Experts with a passion for precision, technical detail, and clear written and verbal communication will find this engagement both rewarding and influential.
Scope of Work
• Evaluate and solve advanced materials science and engineering problems spanning materials selection, failure analysis, testing, and process development.
• Review and assess AI-generated technical responses for technical correctness, accuracy, and engineering quality.
• Apply practical engineering judgment to real-world materials selection and failure scenarios and theoretical questions.
• Analyze and troubleshoot materials issues using industry-standard techniques such as microstructural analysis, fractography, mechanical testing (tensile, fatigue, hardness), and thermodynamic/kinetic modeling.
• Contribute domain expertise to inform model improvements and refine AI reasoning in materials science and engineering.
• Document feedback and provide clear written and verbal explanations to enhance AI learning and performance.
• Collaborate remotely with a distributed team of experts and project participants.
Preferred Qualifications
• Bachelor's degree and above in Materials Science & Engineering, Metallurgy, or ME/ChemE with a materials specialization.
• At least 3 years of hands-on experience in materials selection, failure analysis, testing, or process development.
• Expert knowledge of material classes (metals, polymers, ceramics, composites), microstructure-property relationships, and degradation/failure mechanisms.
• Proven ability to analyze root-cause failures using fractography, metallography, and mechanical/thermal test data.
• Familiarity with relevant standards bodies (ASTM, ASM, ISO materials standards) and process qualification requirements.
• Professional English proficiency with exceptional written and verbal communication skills.
• Experience with materials characterization equipment (SEM, XRD, TEM, DSC/TGA) or process development in a manufacturing environment is a plus.
• Strongly preferred — evidence of problem-authoring rigor, such as: service on an olympiad problem committee; a textbook problem-set or solutions-manual author credit; an item-writer role on an NCEES or academic qualifying-exam committee; service on an ASTM, ASM, or ISO materials standards committee; a top placement or team-lead role in the TMS Materials Bowl, ASM International undergraduate design competition, or an MRS student award; a best-paper award at TMS Annual Meeting, MS&T, or MRS, or a publication in Acta Materialia / Nature Materials; an Acta Materialia Silver Medal; or an early-career fellowship/grant (NSF CAREER, DOE Early Career, Sloan Fellowship) or TMS Young Leader / MRS Outstanding Young Investigator recognition.
Compensation Structure
Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.
Start Timeline & Availability
We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.
Skills mentioned
materials science expertisematerials characterizationtechnical literature reviewscientific data interpretationclear written and verbal communicationremote collaborationannotating technical datasetsscenario and case study developmentquality assurance in scientific deliverablesanalytical skills
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