Life Sciences Scientific Reasoning Expert

OpenTrain AI, Inc.; États-Unis; À distance; USD 41 000 - 83 000; Temps partiel

Résumé du poste

OpenTrain AI, Inc. is seeking a Life Sciences Scientific Reasoning Expert for a remote contract role requiring strong biological research expertise and 20+ hours per week.

You will craft advanced reasoning tasks from real-world data and literature-style material. The work emphasizes deep biological judgment over coding, with responsibilities including designing challenging questions, defending conclusions, and reviewing AI responses for accuracy and clarity.

Qualifications

• Ph.D. or equivalent advanced research experience in life sciences.
• Strong knowledge in molecular biology, genetics, or related fields.
• Ability to interpret experiments and datasets.
• Experience designing assessment tasks is a plus.

Responsabilités

• Design original life sciences problems requiring advanced interpretation.
• Write precise questions and defensible answers.
• Check claims, calculations, assumptions for accuracy.
• Identify ambiguity and alternative valid interpretations.
• Document tasks clearly and incorporate reviewer feedback.
• Create problems that distinguish AI models by reasoning from evidence.

Connaissances

Biological research expertise

Scientific reasoning

English fluency

Communication skills

Assessment design experience

Formation

Ph.D. in Life Sciences or related field

Postdoctoral experience

Description du poste

Life Sciences Scientific Reasoning Expert

Create advanced, non-code life sciences reasoning tasks and expert reference answers for AI evaluation. This remote contract role requires strong biological research expertise and at least 20 hours per week.

100% Remote

Contract, part-time

Engagement

Remote

Location

Oct 20, 2026

Posted

The Work

You will create challenging scientific reasoning tasks based on realistic biological research scenarios. Your work will use evidence such as experimental findings, datasets, figures, sequences, structures, and literature-style material to test whether AI systems can interpret and reason about science.

The role focuses on deep biological judgment rather than programming or simple factual recall. You will also review work for accuracy, clarity, and reproducibility.

• Design original life sciences problems that require advanced interpretation and scientific reasoning.
• Write precise questions and rigorous reference answers with objectively defensible conclusions.
• Check biological claims, calculations, assumptions, and conclusions for scientific accuracy.
• Find ambiguity, alternative valid interpretations, and questions that lack enough constraints.
• Document tasks clearly, incorporate reviewer feedback, and maintain quality.
• Create problems that distinguish AI models by their ability to reason from scientific evidence.

What It Pays and Takes

• Pay: The source does not provide a rate.
• Time: 20 or more hours per week.
• Location: Remote and open worldwide.
• Language: Professional fluency in English.
• Education or experience: A Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences.
• Subject expertise: Strong knowledge of at least one area such as molecular biology, genetics and genomics, cell biology, biochemistry, neuroscience, microbiology, immunology, ecology, structural biology, biophysics, developmental biology, cancer biology, or pharmacology.
• Core skill: Ability to interpret scientific experiments, biological data, and research findings.
• Communication skill: Ability to turn complex scientific concepts into precise, self-contained problems with defensible answers.
• Helpful background: Graduate-level assessment design, manuscript or research review, evaluation of AI-generated scientific responses, or work with AI and machine learning evaluation datasets.
• Also valued: Publications in peer-reviewed journals or conferences, and graduate-level teaching or mentoring experience.
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Requirements

• Experience: Entry level

About AI Training Work

AI training is the human work behind systems that learn from examples, including writing and reviewing model responses and evaluating scientific reasoning. People with deep subject knowledge help create reliable examples and judgments that improve how these systems handle specialized questions.