Materials Scientist / Engineer (AI Training Project)
About Us
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
Role Overview
We are looking for an experienced Materials Scientist or Materials Engineer to analyse experimental results, review technical literature, structure scientific datasets, and create realistic materials-engineering scenarios. You will also evaluate scientific deliverables for accuracy, consistency, and technical quality. Previous AI experience is not required.
CONTRACT: Freelance contractor, paid per completed task
COMMITMENT: Flexible, based on available tasks and project demand
LOCATIONS: Fully remote - GLOBAL
PROCESS: Application review, subject-matter assessment, and onboarding
HOURLY RATE: $80-$130/h
NOTES: Selected experts should be ready to begin within 24–48 hours after onboarding.
Responsibilities
- Analyse experimental results, technical datasets, and materials-science research.
- Conduct literature reviews on materials, methodologies, and characterisation techniques.
- Annotate and structure scientific datasets with clear technical context.
- Interpret material properties, behaviour, and testing results.
- Develop realistic case studies and engineering scenarios.
- Explain complex materials concepts in clear written form.
- Review scientific deliverables for accuracy and consistency.
- Identify gaps, unsupported conclusions, and data-quality issues.
- Collaborate remotely with other scientific contributors.
Requirements
- MS or PhD in Materials Science, Metallurgy, Mechanical Engineering, Chemical Engineering, or a related field.
- Professional or research experience in materials science or engineering.
- Experience with materials characterisation methods such as microscopy, spectroscopy, or mechanical testing.
- Strong experience interpreting scientific data and technical literature.
- Ability to produce clear, accurate, and structured scientific explanations.