Tech Stack

Description

You will work freelance to train large language models by challenging them on drug discovery topics such as ligand-receptor interactions, pharmacokinetics, and in silico docking. Your role involves conversing with the model, verifying factual accuracy, logging errors, and suggesting improvements to prompt engineering and evaluation metrics.

Requirements

• PhD in pharmaceutical sciences, chemistry, pharmacology, or related life-science field
• Experience in medicinal chemistry, pharmacology, high-throughput screening, or ADMET profiling
• Peer-reviewed publications or lab-based discovery research
• Clear metacognitive communication skills

Responsibilities

• Converse with AI models on compound screening strategies and drug design questions
• Verify factual accuracy and logical soundness of model responses
• Capture reproducible error traces and failure modes
• Suggest improvements to prompt engineering and evaluation metrics