Remote Biology AI Data Quality Reviewer

Crossing Hurdles; Ciudad de México; Presencial; MXN 700.000 - 1.100.000; Jornada completa

Descripción de la vacante

Crossing Hurdles is seeking a PhD-level biologist to critically evaluate biology-related data and prompts used in AI development workflows, delivering detailed feedback to improve accuracy and relevance. You will create and refine scientific scenarios and explanations reflecting real-world biological problems, and document methodologies to support transparency in project deliverables.

Collaborate with platform teams to clarify ambiguities, ensure data quality, and promote rigorous scientific

Formación

• PhD in biology or closely related life science discipline.
• Strong ability to contextualize complex topics for diverse audiences.
• Excellent written and verbal communication skills.
• Experience with scientific data analysis and critical evaluation of research.
• Ability to work independently and deliver high-quality outputs.

Responsabilidades

• Critically evaluate and provide detailed feedback on biology-related data, prompts, or content used in AI workflows.
• Create, review, and refine scientific scenarios, questions, or explanations reflecting complex, real-world biological problems.
• Offer expert-level insights to ensure accuracy and depth of AI training datasets.
• Collaborate with platform teams to clarify ambiguities and improve data quality.
• Document findings, methodologies, and rationale to support transparency.
• Participate in iterative review cycles to enhance AI system capabilities in biology.

Conocimientos

Biology expertise

Scientific writing

Critical analysis

Independent work

Educación

PhD in biology or closely related life science

Descripción del empleo

Crossing Hurdles is seeking a PhD-level biologist to critically evaluate biology-related data and prompts used in AI development workflows, delivering detailed feedback to improve accuracy and relevance. You will create and refine scientific scenarios and explanations reflecting real-world biological problems, and document methodologies to support transparency in project deliverables.

Collaborate with platform teams to clarify ambiguities, ensure data quality, and promote rigorous scientific