Materials Science PhD - AI Evaluator

Obsidian; Berlin; Vor Ort; EUR 67.000 - 100.000; Vollzeit

Zusammenfassung

Mercor is seeking PhD and Master’s scientists to author AI evaluation tasks (Sci Code) for a new benchmark in scientific computing. You will source material from papers, datasets, or open sources and design scenarios, prompts, and grading criteria.

Join a collaboration with leading AI labs to challenge frontier models. Engagement spans 6 weeks, part-time (20+ hours/week), with immediate start and a clear interview process.

Qualifikationen

• PhD in materials science, materials engineering, applied physics, chemistry, chemical engineering, or related field.
• Depth in semiconductor materials and molecular modeling.
• Proficiency in Python, R, or other scientific computing language.
• Experience with Git/GitHub and Docker; PR workflow and automated quality checks.

Aufgaben

• Source your own material: a published paper, a Kaggle dataset, an open-source repository, or a scenario you design.
• Write scientific prompts based on the input
• Build the grading criteria that define a correct answer
• Calibrate against frontier models — ensure tasks fail models more often than they succeed

Kenntnisse

Python

R

Scientific computing

Git/GitHub

Docker

Ausbildung

PhD in materials science

Master's degree in related field

Tools

Git

Docker

Jobbeschreibung

Mercor is hiring PhD and Master's scientists to author AI evaluation tasks (Sci Code)

Mercor is partnering with leading AI labs on a new benchmark for scientific computing. You will author original, executable research problems that today's frontier models cannot solve.

Domains — depth required in at least two subdomains (with a coding focus)

• Materials science — semiconductor materials, molecular modeling

What you'll do

• Source your own material: a published paper, a Kaggle dataset, an open-source repository, or a scenario you design
• Write scientific prompts based on the input
• Build the grading criteria that define a correct answer
• Calibrate against frontier models — a task ships only when strong models fail it more often than they succeed

Required

• PhD in materials science, materials engineering, applied physics, chemistry, chemical engineering, or a closely related field
• Demonstrated depth in both of the following subdomains: 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 — authoring runs through a pull-request workflow with automated quality checks

Preferred

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

Engagement

• Duration: 6 weeks
• Commitment: part-time, 20+ hours per week
• Start date: immediate

Process

1. Upload your resume and application form
2. A 25-minute conversational interview covering your background, experience, and motivations
3. Follow up within a few days with next steps and onboarding