In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
Responsibilities
Investigate and analyze the development, morphology, and behavior of microscopic organisms including bacteria, fungi, and algae.
Contribute to the study of the relationship between microorganisms and disease, supporting projects involving medical microbiology.
Assess the impact of antibiotics and other agents on microbial populations, providing insights for AI model accuracy.
Document experimental findings and processes with a focus on clarity for AI training data.
Collaborate with interdisciplinary teams to ensure scientific rigor and data integrity in AI development.
Provide written and verbal expertise on microbiological phenomena and their relevance to real-world and computational contexts.
Utilize rubrics and established evaluation criteria to assess data quality and support AI training workflows.
Required skills
Bachelor’s degree or higher in Biology, Microbiology, Chemistry, or a related field.
Extensive knowledge of bacterial, fungal, and algal systems.
Demonstrated expertise in investigating microbial structure and physiology.
Strong written and verbal communication skills for technical and interdisciplinary collaboration.
Ability to document processes and findings clearly for integration into AI systems.
Comfort working independently in a fully remote, digital-first environment.
Attention to detail and commitment to scientific accuracy.
Preferred skills
Prior experience developing or applying rubrics in scientific or educational contexts.
Experience with AI, machine learning, or annotation projects related to biology or microbiology.
Advanced degree (Master’s or PhD) in a relevant field.
Thriveth makes AI data-training work easier to find, understand, and navigate. We replace uncertainty with clear opportunities, realistic expectations, and insights from real application journeys.
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