Materials Scientist

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

Job summary

SME Careers is seeking a Materials Science Expert (SME) for an hourly, remote contractor role to review AI-generated materials science content and create high-quality reference material with precise feedback.

You will assess accuracy, unit consistency, and engineering reasoning across metals, ceramics, polymers, composites, and nanomaterials, and provide rewrites and model answers to improve AI outputs for training data quality.

Qualifications

• Bachelor’s degree in a materials-related field.
• Excellent English proficiency (C1).
• 3+ years in materials science research or related work.
• Strong understanding of structure–property relationships and materials types.
• Experience with AI data training or technical content review preferred.

Responsibilities

• Review AI-generated materials science content for accuracy and clarity.
• Provide precise written feedback and high-quality rewrites.
• Rate AI responses and rank outputs for quality and usefulness.

Skills

Materials science

Technical writing

Analytical thinking

AI training content

Education

Bachelor’s degree in Materials Science

Tools

AI review tools

Statistical analysis

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.