Remote Life Sciences AI Reasoning Researcher

Gramian Consulting Group; United States; Remote; USD 55,000 - 96,000; Part time

Job summary

Gramian Consulting Group seeks experienced Life Sciences researchers to design advanced scientific reasoning tasks for an AI evaluation benchmark. This is a non-coding contractor role for 6 weeks, remote work open to qualified candidates in multiple countries.

The work focuses on creating self-contained problems from experiments, data, figures, literature evidence, and scenarios, aiming to test genuine scientific interpretation and synthesis.

Qualifications

• PhD, postdoctoral experience, or equivalent advanced research in the Life Sciences.
• Deep expertise in at least one biological discipline.
• Strong ability to interpret experiments, data and research findings.
• Excellent written and oral communication for writing tasks and explanations.

Responsibilities

• Design challenging life-sciences reasoning problems for AI evaluation.
• Create tasks using realistic experiments, datasets, figures, sequences, and literature-style evidence.
• Write questions with clear, defensible answers.
• Develop expert reference answers detailing the required reasoning.
• Ensure tasks test interpretation and synthesis over recall.
• Validate biological claims, calculations, assumptions, and conclusions.
• Identify ambiguity and alternative valid interpretations.
• Build tasks that differentiate advanced AI models by reasoning ability.
• Incorporate reviewer feedback while maintaining quality and throughput.
• Document tasks clearly and reproducibly where applicable.

Skills

Biology expertise

Experimental design

Scientific reasoning

Data interpretation

Education

PhD or equivalent advanced research experience

Job description

Gramian Consulting Group seeks experienced Life Sciences researchers to design advanced scientific reasoning tasks for an AI evaluation benchmark. This is a non-coding contractor role for 6 weeks, remote work open to qualified candidates in multiple countries.

The work focuses on creating self-contained problems from experiments, data, figures, literature evidence, and scenarios, aiming to test genuine scientific interpretation and synthesis.