Mathematics PhD - AI Evaluation Expert - AI Trainer

Mercor; Miami (FL); On-site; USD 83,000 - 138,000; Part time

Job summary

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

Expect depth in multiple subdomains, such as numerical linear algebra, computational mechanics, or computational finance, with strong Python and Docker/GitHub fluency to ensure high-quality, reproducible runs within a PR workflow.

Qualifications

• PhD in mathematics, applied mathematics, computational mathematics, or closely related field.
• Demonstrated depth in at least two of: numerical linear algebra, computational mechanics, computational finance.
• Working proficiency in Python 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 for scientific computing

Git/GitHub proficiency

Docker workflows

Education

PhD in mathematics, applied mathematics, computational mathematics, or closely related field

Master's degree in a related field

Tools

Docker

GitHub

Kaggle

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 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