Mathematics PhD - AI Evaluation Expert - AI Trainer

Obsidian; Los Angeles (CA); On-site; USD 83,000 - 124,000; Full time

Job summary

Mercor is hiring PhD and Master’s scientists to author AI evaluation tasks (Sci Code). You will craft original, executable research problems that current frontier models cannot solve.

Applicants should have a PhD in mathematics or related fields and demonstrated depth in at least two subdomains, plus proficiency in Python or R for scientific computing. Familiarity with Docker and Git/GitHub workflows is required. This engagement runs for 6 weeks, part-time (20+ hours/week), with immediate start.

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.
• Comfortable with Git/GitHub and running code in Docker — authoring runs through a pull-request workflow with automated quality checks.

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 or R for scientific computing

Git/GitHub

Education

PhD in mathematics / related

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

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