Mathematics PhD - AI Evaluation Expert - AI Trainer

Mercor; Los Angeles (CA); On-site; USD 110,000 - 165,000; Part time

Job summary

Mercor is seeking PhD and Master's scientists to author AI evaluation tasks (Sci Code) for a new benchmark in scientific computing. You will author original, executable research problems that today's frontier models cannot solve.

In Los Angeles, this part-time engagement runs 6 weeks with 20+ hours per week. You will source materials, write scientific prompts, build grading criteria, and calibrate against frontier models to ensure tasks fail strong models more often than they succeed.

Qualifications

• PhD in mathematics, applied mathematics, computational mathematics, or a closely related field.
• Demonstrated depth in at least two of: numerical linear algebra, computational mechanics, computational finance.
• Working proficiency in Python or R for scientific computing; experience with Docker and GitHub workflows.

Responsibilities

• 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.

Skills

Python

R

Numerical linear algebra

Computational mechanics

Computational finance

Education

PhD in mathematics / applied mathematics

Tools

Git/GitHub

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. A 25-minute conversational interview covering your background, experience, and motivations

2. Follow up within a few days with next steps and onboarding