Biology AI Data Quality Engineer

Crossing Hurdles; Turkey; On-site; TRY 600,000 - 800,000; Full time

Job summary

Crossing Hurdles is seeking a biology specialist to critically evaluate AI-related biology data and prompts, ensuring accuracy and relevance for advanced training workflows. The role involves creating and refining real-world biological scenarios, documenting methodologies, and collaborating with platform teams to improve data quality and coverage.

Strong communication and independent work ethic are essential for delivering high-quality outputs on schedule.

Qualifications

• PhD in biology or related life sciences.
• Expertise in core biological principles and the ability to contextualize complex topics.
• Excellent written and verbal communication for feedback and reasoning.
• Experience with scientific data analysis and critical evaluation of research.
• Ability to work independently and deliver high-quality outputs on schedule.

Responsibilities

• Critically evaluate and provide detailed feedback on biology-related data, prompts, or content used in AI development workflows.
• Create, review, and refine scientific scenarios, questions, or explanations reflecting complex, real-world biological problems.
• Offer expert-level insights to ensure accuracy, relevance, and depth of AI training datasets and system outputs in the biological domain.
• Collaborate with platform teams to clarify ambiguities and improve data quality, coverage, and representativeness for biological subject matter.
• Document findings, methodologies, and rationale to support transparency and reproducibility in project deliverables.
• Participate in iterative review cycles and ongoing evaluation to enhance the AI system's capabilities in biology.

Skills

Scientific writing

Critical thinking

Communication skills

Independent work

Education

PhD in biology

Tools

Statistical software

Job description

Crossing Hurdles is seeking a biology specialist to critically evaluate AI-related biology data and prompts, ensuring accuracy and relevance for advanced training workflows. The role involves creating and refining real-world biological scenarios, documenting methodologies, and collaborating with platform teams to improve data quality and coverage.

Strong communication and independent work ethic are essential for delivering high-quality outputs on schedule.