Materials Scientist for AI Model Training

Provide domain expertise in materials science and engineering to create high-quality scientific inputs used to train next-generation AI systems. This remote contractor role focuses on interpreting experimental results, annotating technical datasets, developing realistic scenarios, and assuring the quality of scientific deliverables. No prior AI experience is required, your materials domain knowledge is the primary requirement.

• Analyze and interpret scientific data from experiments, 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 contextual notes to support model training.
• Develop realistic scenarios and case studies that reflect practical applications and challenges in materials engineering, metallurgy, or related sectors.
• Provide clear, well organized written and verbal explanations of materials phenomena, properties, and scientific reasoning.
• Perform quality assurance, reviewing scientific deliverables for consistency, accuracy, and adherence to project specifications.
• Collaborate remotely with other scientific contributors, using digital tools and documentation practices.

• Required skills: deep materials science expertise, experience with materials characterization, technical literature review, scientific data interpretation, clear written and verbal communication, remote collaboration, annotating technical datasets, scenario and case study development, quality assurance of scientific deliverables, and strong analytical skills.
• Preferred education: MS or PhD in Materials Science and Engineering, Metallurgy, Mechanical Engineering, Chemical Engineering, or a related discipline with a materials specialization.
• Preferred technical experience: familiarity with characterization techniques such as microscopy, spectroscopy, and mechanical testing.
• Proven ability to summarize literature findings and communicate complex technical concepts to diverse audiences.
• Experience with quality review of scientific documents or datasets and comfort with remote collaboration tools and knowledge sharing environments.

• Engagement type: independent contractor, remote.
• Compensation model: output based, paid per task that meets project specifications.
• Minimum submission requirements apply, experts must submit a minimum of tasks per week.
• Time required to complete tasks will vary by expert experience and workflow.
• Typical timeline: roles are often filled within 48 hours; if selected, you should be prepared to begin your first tasks within 24 to 48 hours after completing onboarding.

• Pay rate: $80 to $130 per hour equivalent, paid on a per task basis according to project specifications.
• Payment is tied to deliverables that meet the project''s quality and formatting requirements.

• Must be able to work as an independent contractor on remote projects.
• Availability to start quickly is required, given the short fill and onboarding timelines described above.