Remote | STEM Expert — $100–$150/hour

24-Mag•New York, NY

22h•Remote

About The Position

24-MAG LLC is recruiting experienced STEM and scientific-domain professionals for a specialised, part-time consulting engagement supporting an advanced AI training and technical evaluation project. Professionals will solve, review, and validate challenging scientific or mathematical problems in their area of expertise, develop reproducible reference solutions, and assess AI-generated answers for technical correctness. Deep expertise in one relevant technical field is sufficient; expertise across every STEM discipline is not expected. The work includes scientific problem solving, evaluation of AI-generated outputs, Python-based analysis and validation, and development of clear reference solutions. The engagement is fully remote and open worldwide, with an expected commitment of approximately 15 hours per week and a flexible schedule, including evenings or weekends. Compensation is $100–$150 per hour and is output-based, with payment for tasks that meet project specifications. Minimum submission requirements apply. The selection process may include screening questions, an approximately 30-minute AI interview, and hiring-manager review. Selected professionals should be prepared to begin their first task within approximately 24–48 hours after onboarding. Roles are typically filled within approximately 48 hours. Work must not use confidential or proprietary information belonging to an employer, client, institution, research group, or other third party. The opportunity is available through 24-MAG LLC, which connects experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams. Applicant information may be processed for recruitment and opportunity matching in accordance with the company’s Privacy Policy.

Requirements

Deep expertise in at least one relevant STEM or scientific domain.
Practical proficiency with Python.
Strong scientific, mathematical, or quantitative problem-solving skills.
Ability to reason carefully about assumptions, constraints, edge cases, and sources of error.
Experience validating, reviewing, or troubleshooting scientific or technical work.
Ability to interpret quantitative data, technical material, and scientific evidence.
Strong written communication skills and ability to explain complex technical concepts clearly.
Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, research group, or other third party.

Nice To Haves

Relevant expertise may be in Chemistry, Biology, Mathematics, Physics, Earth Science, Geoscience, Environmental Science, Robotics, or a closely related quantitative field.
Python tools such as NumPy, SciPy, pandas, SymPy, Jupyter, or domain-specific scientific libraries may be relevant.
Expertise may be demonstrated through academic research, industry, teaching, engineering, independent technical work, or other professional experience.
A specific degree or fixed number of years of experience is not required when strong domain expertise can otherwise be demonstrated.
Prior experience in AI training or model evaluation is not required.

Responsibilities

Solve complex scientific, mathematical, or technical problems within the professional’s primary area of expertise.
Apply rigorous reasoning to assumptions, constraints, edge cases, and potential sources of error.
Develop technically defensible solutions grounded in appropriate scientific principles.
Interpret equations, experimental results, datasets, technical diagrams, or scientific literature where relevant.
Perform sanity checks to determine whether analytical and numerical results are plausible.
Review AI-generated technical answers for factual, mathematical, and scientific correctness.
Identify incorrect assumptions, reasoning errors, missing constraints, and subtle technical mistakes.
Assess whether conclusions are supported by calculations, evidence, or scientific reasoning; compare alternative approaches and determine appropriate methods.
Explain corrections and technical weaknesses clearly and precisely.
Use Python for scientific computation, data analysis, simulations, or solution validation, applying relevant scientific or domain-specific tools.
Develop reproducible computational workflows and validate calculations through numerical or symbolic methods where appropriate.
Document computational reasoning for independent review.
Develop clear, reproducible reference solutions, systematically documenting assumptions, methods, calculations, and conclusions.
Explain technical concepts and corrections clearly in writing, distinguish supported conclusions from uncertain or unsupported claims, and maintain accuracy, reproducibility, and technical clarity.