Train AI on Biological Reasoning.
The models shaping the future of AI are only as rigorous as the biologists behind them. Apply your expertise where it matters, and get paid to do it.
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This is not busywork.
The most advanced language models still hallucinate enzyme mechanisms, confuse taxa, and invent gene functions. You're the biological filter that catches what machines can't — one annotation at a time.
Three steps.
No overhead.
Apply & Qualify
Complete a short biology assessment. We evaluate your ability to interpret experimental data, identify flawed biological reasoning, and apply mechanistic thinking. No resume required — your knowledge speaks.
Get Matched
Based on your background and assessment results, you're matched to projects in your areas of strength. Molecular biology, genomics, ecology, neuroscience, biochemistry; you choose what fits.
Work & Get Paid
Complete tasks on your own schedule. Each task has clear specifications and a defined scope. Payment is per-task, processed weekly, starting at $40/hour.
What you'll actually do.
Write a prompt
Ask questions that test the model's understanding across biological disciplines.
Review AI output
Evaluate model-generated answers line by line. Identify misattributed gene functions, confused signaling pathways, and incorrect interpretations of experimental data.
Evaluate the model's response for scientific accuracy.
Write the correct solution
Author a complete, rigorous response that demonstrates the correct biological reasoning. This becomes training signal for the next model generation.
Provide an answer that reflects the standard of peer-reviewed literature.
Where models need you most.
These are the areas where current AI systems consistently struggle. Your expertise directly addresses the hardest open problems in biological reasoning.
Proof-Based Reasoning
Evaluate and construct formal biological arguments. Identify logical gaps, invalid inferences, and incomplete mechanistic reasoning in model-generated explanations.
Multi-Step Problem Solving
Review complex biological processes requiring sequential reasoning. Check that each step follows validly from the last and that no intermediate mechanisms are skipped.
Symbolic Logic & Formalization
Assess the model's ability to translate natural language into formal biological frameworks and reason within established scientific systems.
Edge Cases & Counterexamples
Identify where models fail on edge cases, degenerate biological scenarios, and subtle counterexamples that break general claims.
Quantitative Modeling
Evaluate applied biology problems involving experimental design, statistical reasoning, and biological modeling of real-world systems.
Research-grade standards.
We require excellence, just as you would require in peer review. The researchers and academics on our platform aren’t here to tick boxes. They’re here because the quality standard matters to them.
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“This is evaluation by people who understand system behavior and edge cases. The focus is always on improving model reliability.”
Daniel
Research Engineer
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“Every decision is grounded in first-principles reasoning. The standards are comparable to what I’ve seen in academic and industrial research.”
Anita
Applied Scientist
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“This is annotation done by people who understand models, not just labels. The quality bar is high and enforced.”
Elena
Senior ML Engineer
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“The work demands subject-matter expertise. Decisions are justified, reviewed, and aligned to well-defined taxonomies.”
Stephen
Research Scientist
Biology tasks completed weekly
Active Biology contributors
University Biology departments represented
Built for people who think in theories.
Graduate Students
PhD and Master's candidates in molecular biology, cell biology, or related life sciences.
Olympiad Competitors
National or international competition experience in biological problem solving.
Research Biologists
Active or former researchers applying deep expertise to AI reasoning.
Applied Biology Professionals
Clinicians, biotech professionals, and data scientists with rigorous biological foundations.
Frequently Asked Questions
Have questions? Here we answer the most common questions.
You’ll work on tasks that help AI perform better—like reviewing responses, checking accuracy, refining prompts, and rating outputs. No coding or technical background needed.
You’ll work on projects that improve AI systems, such as reviewing responses, checking accuracy, refining prompts, ranking outputs, and validating model behavior.
Projects include data labeling, annotation, evaluation, and quality review across text, images, and structured tasks—focused on improving real production AI systems.
This is ideal for people with strong reasoning skills, attention to detail, and subject-matter expertise who want flexible, remote work with real impact.
This is a flexible, task-based contractor role. There is no long-term commitment required, and you can work as much or as little as you choose.
Available projects vary but commonly include prompt evaluation, response ranking, factual accuracy checks, domain-specific review, and structured annotation tasks.
Researchers, students, professionals, and independent contributors who enjoy analytical work and want to contribute to advancing AI systems.
This is remote, asynchronous work focused on output quality rather than hours logged. Performance is evaluated based on contribution quality and consistency.
Get ahead in a changing workforce.
No recruiters. No interviews. Just meaningful work and real compensation.