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
Review and evaluate pharmacological content for accuracy, clarity, and real-world relevance.
Develop and curate scenarios, case studies, and datasets related to pharmacokinetics, pharmacodynamics, drug interactions, and therapeutic applications.
Provide expert feedback on AI-generated responses for pharmacology queries, ensuring scientific rigor and clinical adherence.
Translate complex pharmacology concepts into accessible information for AI model training purposes.
Identify gaps or inaccuracies in existing materials and propose data-driven improvements.
Collaborate with interdisciplinary teams to refine guidelines and datasets in line with the latest pharmacological standards.
Document methodologies and rationales behind pharmacological decisions and recommendations within project deliverables.
Preferred skills
Advanced degree (PharmD, PhD, MD, or equivalent) in Pharmacology, Pharmaceutical Sciences, or a closely related field.
Demonstrated expertise in clinical, molecular, or systems pharmacology.
Experience authoring, reviewing, or teaching pharmacology-related content in academic, clinical, or industry settings.
Familiarity with current global guidelines, regulatory standards, and best practices in drug therapy and safety.
Strong analytical and critical evaluation skills—able to discern nuances in complex pharmacological data.
Ability to communicate complex scientific ideas clearly and concisely to diverse audiences.
Previous participation in digital, AI, or data annotation projects is an asset but not required.
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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