Materials Scientist / Engineer

Skills

About this role

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.

Materials Science, Materials Characterization, Metallurgy, Microscopy & Spectroscopy, Scientific Data Analysis

Responsibilities

• Analyze and interpret scientific data from experimental results, technical datasets, and research publications in materials science and engineering.
• Conduct comprehensive technical literature reviews to identify key advances, methodologies, and challenges in materials characterization and related fields.
• Annotate and structure technical datasets with precise, detailed commentary and context to support AI model training.
• Develop realistic scenarios and case studies that represent practical applications and challenges within materials engineering, metallurgy, or related sectors.
• Provide clear, well-organized written and verbal explanations of materials phenomena, properties, and scientific reasoning.
• Review and assure the quality, consistency, and accuracy of scientific deliverables submitted for project milestones.
• Collaborate remotely with other scientific contributors, leveraging digital tools and documentation practices.

Qualifications

• MS or PhD in Materials Science & Engineering, Metallurgy, Mechanical Engineering, Chemical Engineering, or a related discipline with a materials specialization.
• Demonstrated expertise in materials characterization techniques (e.g., microscopy, spectroscopy, mechanical testing).
• Strong analytical skills and experience interpreting complex scientific data.
• Substantial experience conducting technical literature reviews and summarizing key findings.
• Proven ability to communicate complex technical concepts clearly, both in writing and verbally, to diverse audiences.
• Experience with quality assurance and review of scientific documents or datasets.
• Familiarity with remote collaboration tools and digital knowledge-sharing environments.