$80–150/hr · micro1
A STEM expert who solves technical problems in their field and validates AI-generated solutions to help train AI systems in scientific reasoning.
What you would do
- Tackle intricate scientific, mathematical, or technical challenges within your area of specialized expertise
- Evaluate AI-generated solutions for technical, computational, and scientific accuracy
- Use Python to perform calculations, simulations, data analysis, and solution validation
- Identify and explain technical errors including incorrect assumptions and reasoning flaws
- Develop clear reference solutions demonstrating proper methodology and scientific rigor
Who they want
- Deep expertise in Chemistry, Biology, Mathematics, Physics, Earth Science, Robotics, or related STEM domain
- Practical proficiency with Python and relevant scientific libraries such as NumPy, SciPy, pandas, or SymPy
- Strong quantitative and scientific problem-solving abilities with attention to edge cases
- Experience validating, reviewing, or troubleshooting technical work in your field
- Ability to explain complex technical concepts clearly and identify sources of error
Main skills
What the interview asks about
1.Python-based scientific problem solving
The role is heavily coding-focused; the interviewer evaluates your ability to translate domain problems into executable Python code and validate computational results.
For example: “Describe a recent scientific calculation or simulation you implemented in Python - what libraries did you use, and what validation checks did you perform to verify accuracy?”
2.Error detection in technical solutions
Evaluating AI-generated solutions requires detecting subtle mistakes; the interviewer probes your capacity to identify errors in logic, assumptions, and calculations.
For example: “Tell us about a technical solution where the methodology was sound but a flawed assumption led to incorrect results - what was the assumption, and how did you catch it?”
3.Domain expertise application
Your deep knowledge in a specific field enables accurate assessment of correctness; the interviewer checks how you evaluate solutions beyond surface-level accuracy.
For example: “Explain a recent problem from your field where ignoring a real-world constraint or physical principle would produce an answer that seems mathematically correct but is actually wrong.”
4.Technical materials interpretation
STEM experts must interpret equations, experimental results, and technical diagrams accurately; the interviewer gauges your comfort with diverse technical materials.
For example: “Describe a technical diagram, experimental dataset, or peer-reviewed paper you recently analyzed; what details did you extract, and what could someone less familiar with the field miss?”
5.Rigorous result validation
Rigorous validation distinguishes expert problem-solving from superficial analysis; the interviewer evaluates whether you design comprehensive checks for solution quality.
For example: “For a complex scientific solution you recently verified, describe the specific validation checks you performed and what would have flagged a potential error.”
6.Comparing alternative approaches
Multiple valid methods may exist for a problem; the interviewer probes your judgment about when alternative approaches agree, diverge, or produce complementary insights.
For example: “Describe two different methods you've applied to solve the same technical problem - how did you verify consistency, and under what conditions would you choose one over the other?”
A task you may get
Analyze a scientific solution using Python, identify at least one technical error or problematic assumption, and explain your correction using code and clear reasoning.
How to prepare
- Review your strongest STEM domain and prepare 2-3 recent problems you solved with Python code
- Practice explaining a technical error in a scientific solution to someone from a different field
- Refresh your knowledge of validation approaches for scientific computation: bounds checking, alternative methods, and sanity testing
- Prepare examples of edge cases or constraints that are often overlooked in mathematical or physical reasoning
The facts
- Pay
- $80–150/hr
- Open to
- Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia
- Field
- Other
- Role type
- Expert
- Posted
- 9/1/2026
- Places left
- 10
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