Materials Science PhD - AI Evaluator

mercor; United States; Remote; USD 87,000 - 110,000; Part time

Job summary

Mercor connects elite creative and technical talent with leading AI research labs. Mercor seeks a Material Science PhD Coding Expert for a remote, six-week contract (20+ hours/week) to support AI research labs.

You will source materials from papers and datasets, write scientific prompts, and build grading criteria to assess model performance, calibrating tasks so strong models fail appropriately. Immediate start preferred.

Qualifications

• PhD in materials science, materials engineering, applied physics, chemistry, chemical engineering, or a closely related field.
• Demonstrated depth in semiconductor materials and molecular modeling.
• Working proficiency in Python, R, or another relevant programming language for scientific computing.
• Comfortable with Git/GitHub and running code in Docker.
• Publications in peer-reviewed journals preferred.

Responsibilities

• Source material from published papers, Kaggle datasets, open-source repositories, or designed scenarios.
• Write scientific prompts based on sourced input to challenge AI models.
• Build grading criteria to define correct answers for scientific tasks.
• Calibrate tasks against frontier models, ensuring tasks ship only when strong models fail more often than succeed.
• Work independently and asynchronously to meet deadlines and improve AI model performance.

Skills

Python

R

Scientific computing

Education

PhD in materials science or related

Tools

GitHub

Docker

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey .

Position: Material Science PhD Coding Experts Type: Contract Compensation: $70/hour Location: Remote Duration: 6 weeks Commitment: 20+ hours/week

Role Responsibilities

• Source material from published papers, Kaggle datasets, open-source repositories, or designed scenarios.
• Write scientific prompts based on sourced input to challenge AI models .
• Build grading criteria to define correct answers for scientific tasks.
• Calibrate tasks against frontier models, ensuring tasks ship only when strong models fail more often than succeed.
• Work independently and asynchronously to meet deadlines and improve AI model performance .

Qualifications

Must-Have

• PhD in materials science, materials engineering, applied physics, chemistry, chemical engineering, or a closely related field.
• Demonstrated depth in semiconductor materials and molecular modeling.
• Working proficiency in Python , R , or another relevant programming language for scientific computing.
• Comfortable with Git/GitHub and running code in Docker .

Preferred

• Publications in peer-reviewed journals.
• Prior scientific software or research engineering experience.

Start Date

• Immediate