Biomedical Evidence Evaluator

Evaluate whether AI-generated biomedical content is factually grounded, appropriately qualified and supported by the supplied evidence.

Key responsibilities

• Score factuality and evidence use.
• Identify overstatement and unsupported causal claims.
• Compare competing responses.
• Write structured rationales for preference decisions.

What we look for

• Biomedical or life-science degree with strong research literacy.

How success is measured

Factuality agreement, evidence accuracy, false-positive/negative balance.

How this works on Jwuma

Candidate profile → skills evidence → domain assessment → calibration task → qualification → project matching → production → peer review → expert QA → performance feedback and progression. Corpshore AI service alignment Applicable across annotation and labeling, RLHF and preference data, model evaluation and red-teaming, multimodal datasets and specialized AI data operations, depending on project scope.

Common questions