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This role involves assessing large language model (LLM) responses related to biology for scientific accuracy and quality. You will refine prompts, fact-check information, and provide detailed annotations to improve model performance in life sciences contexts.
Responsibilities
- Write and refine prompts to guide model behavior in the life sciences context
- Evaluate LLM-generated responses to biology-related queries for scientific accuracy and reasoning quality
- Conduct fact-checking using authoritative public sources and domain knowledge
- Annotate model responses by identifying strengths, areas of improvement, and factual or conceptual inaccuracies
- Assess clarity, structure, and appropriateness of explanations for different audiences
- Ensure model responses align with expected conversational behavior and system guidelines
Requirements
- PhD in Biology or a closely related life sciences field
- Deep expertise in one or more of the following sub-domains: Molecular & Cellular Biology, Organismal Physiology & Development, Microbiology, Immunology & Pathobiology, Ecology, Evolution & Environmental Biology
- Significant experience using large language models (LLMs)
- Excellent writing skills and ability to explain complex life sciences concepts
- Strong attention to detail
- Experience reviewing or editing technical or academic writing
Qualifications
- PhD in Biology or a closely related life sciences field
Nice to Have
- Prior experience with RLHF, model evaluation, or data annotation work
- Experience teaching, mentoring, or explaining life sciences concepts to non-expert audiences
- Familiarity with evaluation rubrics, benchmarks, or structured review frameworks
Skills
Large Language Models (LLMs)
*
RLHF
*
* Required skills
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