AI Evaluation Scientist - Math PhD (6-Week, Part-Time)

Mercor; Miami (FL); Sur place; USD 83 000 - 138 000; Temps partiel

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Python for scientific computing

Git/GitHub proficiency

Docker workflows

Formation

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

Master's degree in a related field

Outils

Docker

GitHub

Kaggle

Description du poste

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.