Materials Scientist

SME Careers; Finland; Remote; EUR 55,000 - 96,000; Part time

Benefits offered by this job

Flexible hours

Remote work

Job summary

SME Careers in Finland is hiring a Materials Science SME for hourly remote contractor work. You will review AI-generated materials science content, evaluate accuracy, terminology, and explanations, and provide precise feedback and rewrites to improve model outputs.

The role requires a strong materials science background, English proficiency at a professional level, and 3+ years of related experience. You will work across time zones to deliver high-quality, rubric-based evaluations and training

Qualifications

• Bachelor’s degree or higher in materials science, materials engineering, or related field.
• Minimum 3 years of materials science research, engineering, or QA experience.

Responsibilities

• Develop AI training content across metals, ceramics, polymers, composites, phase diagrams, and characterization.
• Optimize AI outputs for accuracy, clarity, and relevance to prompts.
• Ensure model integrity by detecting incorrect claims and unsafe design assumptions.

Skills

Materials science expertise

English proficiency

AI data training experience

Attention to detail

Technical writing

Education

Bachelor’s degree in Materials Science or related field

3+ years of relevant experience

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.