Remote Materials Scientist for AI Training & Evaluation

Alignerr Corp.; Sydney; Remote; AUD 14,000 - 55,000; Part time

Job summary

Alignerr is seeking a Materials Science Specialist (AI Training) to help train and evaluate AI models on advanced scientific content. Masters and PhDs in Materials Science or related fields are welcome for remote collaboration.

You will develop and review complex materials science problems, apply semiconductor materials or molecular modeling expertise, and assess AI reasoning for accuracy and depth. The role offers flexible, task-based work with 10–40 hours per week.

Qualifications

• Master's or PhD in Materials Science or a closely related discipline.
• Strong domain expertise in semiconductor materials, molecular modeling, or related areas.
• Excellent written communication and scientific rigor.
• Based in the US, Canada, UK, Australia or New Zealand for remote collaboration.

Responsibilities

• Develop and review advanced materials science problems with real-world relevance.
• Craft complex problem statements using semiconductor materials or molecular modeling.
• Evaluate AI-generated scientific reasoning for accuracy and depth.
• Collaborate asynchronously with AI researchers to advance AI scientific reasoning.
• Ensure clarity and rigor across all deliverables.
• Work independently on task-based assignments with flexible scheduling.

Skills

Materials Science expertise

Academic research experience

Detail-oriented

Written communication

Independent work

Education

Master's or PhD in Materials Science or related field

Tools

Python

MATLAB

Job description

Alignerr is seeking a Materials Science Specialist (AI Training) to help train and evaluate AI models on advanced scientific content. Masters and PhDs in Materials Science or related fields are welcome for remote collaboration.

You will develop and review complex materials science problems, apply semiconductor materials or molecular modeling expertise, and assess AI reasoning for accuracy and depth. The role offers flexible, task-based work with 10–40 hours per week.