Remote Geospatial AI Reviewer (PhD)

Healing Strides Counseling; United States; Remote; USD 83,000 - 117,000; Part time

Job summary

AuraOne seeks PhD-level computational biologists to remotely review AI outputs in geospatial science, assess derivations, reproduce results, and document correct methods for training the modeling team. You will adjudicate disputes using textbooks and papers and maintain calibration through inter-rater scoring.

Ideal candidates have graduate-level training in geospatial science, publication experience, and the ability to reason clearly with cited sources.

Qualifications

• Graduate-level training or equivalent applied experience in geospatial science or a closely related field for PhD-Level Computational Biologists work.
• Hands-on experience publishing, teaching, or advising on the topic at a professional level.
• Clear written reasoning that cites methods, papers, or worked examples.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current geospatial science methods, conventions, and prior work for PhD-Level Computational Biologists assignments.
• Reproduce or sanity-check key derivations, calculations, or experimental claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture the corrected reasoning or worked example so the modeling team can train on it.
• Adjudicate disputed answers against textbooks, papers, or community standards.
• Maintain reviewer-quality scores in inter-rater calibration cycles.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Geospatial science

Python

Education

PhD-level training

Tools

Python

Job description

AuraOne seeks PhD-level computational biologists to remotely review AI outputs in geospatial science, assess derivations, reproduce results, and document correct methods for training the modeling team. You will adjudicate disputes using textbooks and papers and maintain calibration through inter-rater scoring.

Ideal candidates have graduate-level training in geospatial science, publication experience, and the ability to reason clearly with cited sources.