Biologist Talent Network

What you would do

• Evaluate AI-generated outputs in molecular biology, cellular processes, and lab procedures for accuracy and scientific validity
• Create realistic biological scenarios and test cases to validate AI understanding of experimental design and proper protocols
• Analyze life science datasets to determine whether AI models correctly interpret data patterns and recognize statistical constraints
• Document domain-specific feedback explaining why certain model outputs contradict established biological principles
• Support research teams in refining AI systems through targeted, evidence-based critical assessment

Who they want

• Professional background in molecular biology, cellular biology, or equivalent experimental sciences
• Proven experience with experimental design, lab research methodology, and biological data analysis
• Strong written and verbal communication skills to explain technical concepts to non-biology audiences
• Track record of independent work and self-directed project management in remote environments
• Availability for 15-30 hours weekly on flexible contract arrangements

Main skills

What the interview asks about

1. 1.Experimental design validation

   Assessing whether AI systems understand proper methodology is critical since errors here could mislead researchers and compromise model quality.

   For example: “Describe a common procedural mistake in protein purification and explain how you would evaluate whether an AI system's methodology avoids this pitfall.”

2. 2.Data pattern recognition

   AI models must correctly interpret biological datasets; your judgment determines whether models recognize genuine relationships versus spurious correlations.

   For example: “An AI analysis of a 150-sample gene expression study reports 2.1-fold upregulation with p-value 0.03. What specific concerns would you raise before accepting this result?”

3. 3.Technical concept translation

   Conveying complex biology in clear feedback helps AI researchers understand domain constraints and prioritize improvements more effectively.

   For example: “An AI system generated a scenario involving protein synthesis that contains a thermodynamic error. How would you explain the significance to a team without biology backgrounds?”

4. 4.Biological assumption validation

   Recognizing which model outputs reflect authentic biological constraints versus plausible-sounding mistakes determines the reliability of training data quality.

   For example: “Review an AI-generated description of membrane transport in cancer cells. What aspects require verification against current molecular literature?”

5. 5.Scenario realism assessment

   Well-designed test cases reveal whether AI systems grasp edge cases and boundary conditions that distinguish expert from superficial understanding.

   For example: “Create a test scenario that would expose whether an AI model understands how pH changes affect enzyme kinetics in different cellular compartments.”

A task you may get

Given an AI description of antibiotic resistance mechanisms in bacteria, identify factual errors, suggest corrections, and explain why accurate domain knowledge matters for developing robust AI systems in biology.

How to prepare

• Review recent AI applications in genomics, drug discovery, and biomarker research to understand current model capabilities and common failure modes
• Prepare concrete examples from your own research demonstrating how you evaluate methodological rigor and troubleshoot experimental complications
• Study published critiques of AI reasoning about biological systems, focusing on where models typically falter with causation or pathway dynamics
• Practice explaining molecular and cellular concepts clearly to non-specialists, emphasizing relevant details without jargon

The facts

Pay

$60–80/hr

Hours

Part time

Where

Remote

Field

Life, Physical, and Social Science

Role type

Talent network

Posted

2/27/2026

We wrote this page from the public Mercor listing. It may be out of date, so read the full posting before you apply.