Mathematics PhD: Scientific AI Prompt Engineer (Part-Time)

RemoteLeads; United States; Remote; USD 83,000 - 117,000; Part time

Benefits offered by this job

Competitive pay at $70 per hour

Six-week part-time engagement

Opportunity to contribute to AIbench

Reasonable accommodations available on

Job summary

RemoteLeads is seeking a senior-level researcher for a six-week, part-time engagement in the United States. You will source material from research papers and datasets, write scientific computing prompts, and help design grading criteria for correct solutions.

You will calibrate tasks against frontier AI models and release them only when models frequently fail. You will submit work via GitHub pull requests with automated quality checks and collaborate with peers.

Qualifications

• PhD in mathematics, applied mathematics, computational mathematics, or a closely related field.
• Depth in at least two of numerical linear algebra, computational mechanics, or computational finance.
• Working proficiency in Python or R for scientific computing.
• Comfort with Git/GitHub and running code in Docker.
• Peer-reviewed journal publications preferred.
• Prior scientific software or research engineering experience preferred.
• Availability for part-time work of 20+ hours per week over six weeks.
• Ability to start immediately.

Responsibilities

• Source material from research papers, Kaggle datasets, open-source repositories, or original scenarios.
• Write scientific computing prompts based on the selected material.
• Develop grading criteria that define correct solutions.
• Calibrate tasks against frontier AI models and release them only when models frequently fail.
• Submit authored work through a GitHub pull-request workflow with automated quality checks.

Skills

Python

R

Git/GitHub

Education

PhD in mathematics / applied mathematics / computational mathematics

Tools

Docker

GitHub

Job description

RemoteLeads is seeking a senior-level researcher for a six-week, part-time engagement in the United States. You will source material from research papers and datasets, write scientific computing prompts, and help design grading criteria for correct solutions.

You will calibrate tasks against frontier AI models and release them only when models frequently fail. You will submit work via GitHub pull requests with automated quality checks and collaborate with peers.