Life Sciences: AI Trainer

Brunel is partnering with DataAnnotation to connect experienced Life Sciences professionals with an innovative AI training program. This opportunity brings together deep scientific expertise and artificial intelligence to help evaluate and improve AI-generated content across the Life Sciences. Selected experts will contribute their knowledge to the development of high-quality, scientifically accurate AI systems.

This opportunity is designed for scientists and researchers with deep technical expertise across drug discovery, computational biology and bioinformatics, or experimental biology.

Engagement Details

Qualifications & Requirements

• Master’s or PhD — or a current PhD candidate — in Biology or a directly related field, including molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology. The degree must be completed in the U.S., Canada, Europe, the UK, or Australia.
• 3+ years of hands-on experience in the relevant subfield. 
• Genuine availability for 30–40+ hours per week across the full 12 weeks.
• Full professional/native-level written and spoken English.
• Strong written communication skills and comfort working independently in a fully remote environment.
• Comfort with ambiguity and strong attention to detail. Ability to orient within a company data package ranging from 100MB to 30GB+ and quickly build an accurate, deep working picture of it, especially when the science sits partly or wholly outside your own specialization.
• Ability to interpret feedback, judge which parts of it are actually correct, and apply it without hand-holding. When stuck, independently look for the answer rather than waiting for one.
• Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete.
• General familiarity with AI/LLM tools; user-level familiarity is sufficient.
• Hands-on experience using large language models in life sciences professional work, with the judgment to distinguish a well-reasoned answer from a plausible-sounding but incorrect one.