Materials Scientist - Fully Remote | Upto $70/hr
Budget / Salary$70–70
TypeContract
LocationSouth Korea
Posted2 hours ago
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
Interview Process
Upload your resume and application form.
A 25-minute conversational interview covering your background, experience, and motivations.
Follow up within a few days with next steps and onboarding.
Application Process (Takes 20–30 mins to complete)
Upload resume
AI interview based on your resume
Submit form
Resources & Support
For details about the interview process and platform information, please check:
For any help or support, reach out to:
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
Originally posted on Himalayas
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
Interview Process
Upload your resume and application form.
A 25-minute conversational interview covering your background, experience, and motivations.
Follow up within a few days with next steps and onboarding.
Application Process (Takes 20–30 mins to complete)
Upload resume
AI interview based on your resume
Submit form
Resources & Support
For details about the interview process and platform information, please check:
For any help or support, reach out to:
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
Originally posted on Himalayas
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