Mathematics PhD - AI Evaluation Expert - AI Trainer

Mercor; Miami (FL); On-site; USD 83,000 - 124,000; Part time

Job summary

Mercor is seeking PhD and Master’s scientists to author AI evaluation tasks for a new benchmark in scientific computing. You will source material, craft executable problems, and build the grading criteria to measure model performance on challenging scenarios.

You will calibrate prompts against frontier models, ensuring tasks remain difficult where state-of-the-art systems struggle, across two subdomains with a coding focus.

Qualifications

• PhD in mathematics, applied mathematics, computational mathematics, or a closely related field.
• Depth in at least two subdomains: numerical linear algebra, computational mechanics, or computational finance.
• Proficiency in Python or R for scientific computing.
• Experience with Git/GitHub and running code in Docker with pull-request workflows.

Responsibilities

• Source your own material from papers, Kaggle datasets, open-source repositories, or scenarios 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.

Skills

Python

R

Git/GitHub

Education

PhD in mathematics / applied mathematics / computational mathematics

Tools

Docker

Job description

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)

• Mathematics — numerical linear algebra, computational mechanics, computational finance

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 mathematics, applied mathematics, computational mathematics, or a closely related field
• Demonstrated depth in at least two of the following subdomains: numerical linear algebra, computational mechanics, computational finance
• Working proficiency in Python or R 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