Biology AI Trainer

When AI gets the details wrong in your field, real people pay for it. The people who know the work best have to be the ones training these models. You’re not being replaced by AI. You’re making sure it’s used accurately and safely.

• Design challenging, realistic biology and STEM tasks based on professional practice, including scenarios, colleague-style prompts, and supporting files such as experimental data, lab records, protocols, specifications, simulation outputs, and technical reports.
• Run tasks through frontier AI models and evaluate the resulting analyses, calculations, experimental designs, design reviews, or reports against professional standards.
• Compare model outputs produced from the same prompt and source materials, determine which output performs better, and document strengths and shortcomings.
• Develop detailed grading rubrics defining the assumptions, methods, units, magnitudes, failure modes, acceptance criteria, and evidence required for a correct response.
• Identify and document concrete model failures, including unit or scaling errors, misread data, unsupported conclusions, fabricated or ignored source files, missed safety or boundary conditions, and off-brief interpretations.
• Create and assemble realistic professional files needed to complete tasks, ranging from compact multi-file exercises to larger scenarios developed over several days.
• Review and refine tasks created by other subject matter experts and contribute across biology and adjacent scientific disciplines.
• Propose task concepts based on professional experience, including the scenario, prompt, files to be developed, and anticipated model failure points.

• 2+ years of applied experience preferred in biology, another core science, engineering, data science, or a related technical discipline.
• Currently pursuing or holding a bachelor's degree or higher; 2+ years of experience outside undergraduate study is preferred.
• Recent or current hands‑on individual‑contributor experience performing scientific or technical work, rather than solely managerial experience.
• Experience working with real scientific data and field‑relevant tools, such as instrument or measurement data, laboratory records, simulation outputs, and analysis software.
• Strong experimental and statistical reasoning skills, including experiment design, measurement error, uncertainty, and common statistical pitfalls.
• Ability to write clear, detailed professional reasoning explaining why an analysis or result is correct or incorrect.
• Full professional or native‑level written and spoken English.
• General familiarity with AI and LLM tools and the ability to distinguish well‑reasoned answers from plausible but inaccurate ones.
• Working understanding of adjacent scientific sub‑disciplines sufficient to evaluate work beyond a narrow specialty.
• Coding or scientific computing skills, such as Python, R, MATLAB, or similar tools, are a strong plus and required for some projects.
• Comfort using cloud file tools, managing browser profiles, installing desktop applications, handling files, converting between spreadsheet formats, and creating compressed files for sharing.
• Advanced degrees, professional licensure, or chartered status are a plus but not required.

• Pay of $40-$125 USD per hour, paid via Pay Pal on a regular cadence.
• Fully remote contract work with flexible scheduling.
• Minimum commitment of 10 hours per week with no weekly maximum; many projects prioritize 30-40 hours per week and full‑time hours may be available.
• Multi‑day task timers that allow work to be spread across several days.
• Access to Claude and ChatGPT through the project; no personal subscription is required.
• Regular paid projects in your field after successful assessment and onboarding.
• Opportunity to use professional scientific expertise to improve frontier AI systems.
• Structured feedback, revision opportunities, and the chance for experienced contributors to review other experts’ tasks.