Materials Scientist

SME Careers; New Zealand; Remote; NZD 55,000 - 110,000; Part time

Job summary

SME Careers is seeking a Materials Science Expert (SME) to review AI-generated materials science content and create high-quality reference material. This hourly, remote contractor role emphasizes evaluating accuracy, engineering reasoning, terminology, and clear, precise feedback.

You will assess outputs for correctness, unit consistency, structure-property reasoning, and adherence to prompts, identifying errors in crystal structures, phase diagrams, polymers, metals, and characterization.

Qualifications

• Bachelor’s degree or higher in Materials Science, Materials Engineering, Mechanical Engineering, Chemical Engineering, Metallurgy, Polymer Science, Nanotechnology, or a related field, or equivalent professional experience.
• Strong professional proficiency in English, minimum C1, to follow guidelines and provide detailed feedback in English.
• 3+ years of experience in materials science research, materials engineering, laboratory work, manufacturing, teaching, technical writing, academic review, or related scientific/engineering work.
• Deep understanding of structure-property relationships, crystal structures, phase diagrams, mechanical behavior, heat treatment, polymers, ceramics, metals, composites, corrosion, and characterization techniques.
• Ability to evaluate materials explanations, calculations, diagrams, experimental interpretations, and engineering recommendations for accuracy and clarity.
• Comfortable identifying hallucinated material properties, incorrect processing claims, flawed microstructure reasoning, unsafe engineering suggestions, or unsupported performance claims.
• High attention to detail when reviewing units, material terminology, test methods, process conditions, assumptions, and design tradeoffs.
• Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
• Prior experience with AI data training/annotation, scientific QA, engineering QA, academic QA, technical content review, or rubric-based evaluation is strongly preferred.

Responsibilities

• Develop AI Training Content: Create prompts and gold-standard materials science answers across topics such as metals, ceramics, polymers, composites, phase diagrams, heat treatment, mechanical testing, corrosion, nanomaterials, and characterization.
• Optimize AI Performance: Evaluate and rank AI outputs to improve scientific accuracy, engineering logic, terminology, structure-property reasoning, clarity, and relevance to the user’s prompt.
• Ensure Model Integrity: Detect misleading materials claims, hallucinated properties, flawed processing recommendations, incorrect diagrams, unsafe design assumptions, and overconfident interpretations; validate reliability across materials science use cases.

Skills

English proficiency

Attention to detail

Reliability

Self-direction

Education

Bachelor’s degree or higher in Materials Science or related field

Job description

In this hourly, remote contractor role, you will work as a Materials Science Expert (SME) to review AI-generated materials science content and/or create high-quality materials science reference content, evaluating scientific accuracy, engineering reasoning, terminology, and step-by-step explanations while providing precise written feedback. You will assess outputs for correctness, clarity, physical plausibility, unit consistency, microstructure-property reasoning, experimental context, and adherence to the prompt; identify errors in crystal structure, phase diagrams, mechanical properties, polymers, ceramics, metals, composites, nanomaterials, processing methods, and characterization; fact-check claims when needed; write high-quality rewrites and model answers that demonstrate best practices in materials science explanation; and rate and compare multiple AI responses based on correctness and overall quality.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your materials science expertise directly helps improve the world’s premier AI models by making their materials, engineering, and applied-science outputs more accurate, practical, and clearly explained.

Key Responsibilities

• Develop AI Training Content: Create prompts and gold-standard materials science answers across topics such as metals, ceramics, polymers, composites, phase diagrams, heat treatment, mechanical testing, corrosion, nanomaterials, and characterization.
• Optimize AI Performance: Evaluate and rank AI outputs to improve scientific accuracy, engineering logic, terminology, structure-property reasoning, clarity, and relevance to the user’s prompt.
• Ensure Model Integrity: Detect misleading materials claims, hallucinated properties, flawed processing recommendations, incorrect diagrams, unsafe design assumptions, and overconfident interpretations; validate reliability across materials science use cases.

Your Profile

• Bachelor’s degree or higher in Materials Science, Materials Engineering, Mechanical Engineering, Chemical Engineering, Metallurgy, Polymer Science, Nanotechnology, or a related field, or equivalent professional experience.
• Strong professional proficiency in English, minimum C1, to follow guidelines and provide detailed feedback in English.
• 3+ years of experience in materials science research, materials engineering, laboratory work, manufacturing, teaching, technical writing, academic review, or related scientific/engineering work.
• Deep understanding of structure-property relationships, crystal structures, phase diagrams, mechanical behavior, heat treatment, polymers, ceramics, metals, composites, corrosion, and characterization techniques.
• Ability to evaluate materials explanations, calculations, diagrams, experimental interpretations, and engineering recommendations for accuracy and clarity.
• Comfortable identifying hallucinated material properties, incorrect processing claims, flawed microstructure reasoning, unsafe engineering suggestions, or unsupported performance claims.
• High attention to detail when reviewing units, material terminology, test methods, process conditions, assumptions, and design tradeoffs.
• Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
• Prior experience with AI data training/annotation, scientific QA, engineering QA, academic QA, technical content review, or rubric-based evaluation is strongly preferred.