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
Analyze and annotate complex biological data sets, focusing on applications relevant to medicinal chemistry.
Provide feedback and domain-specific insights to improve AI models in computational biology contexts.
Evaluate scientific content for accuracy, relevance, and clarity, ensuring data aligns with industry standards.
Develop and review problem sets, case studies, or scenarios based on real-world medicinal chemistry challenges.
Collaborate asynchronously with other experts to validate findings and share perspectives on project deliverables.
Contribute to the refinement of data curation methodologies and best practices in computational biology.
Preferred skills
Advanced degree (PhD, PharmD, or MSc) in computational biology, medicinal chemistry, bioinformatics, or a closely related field.
Demonstrated expertise in medicinal chemistry, including experience with drug discovery or design.
Strong analytical skills with a deep understanding of biological datasets and scientific literature.
Experience applying computational methods to solve problems in chemistry or biology.
Proficiency with relevant bioinformatics tools, cheminformatics platforms, or data analysis software.
Excellent written communication skills to clearly explain complex scientific concepts to diverse audiences.
Previous participation in cross-disciplinary or AI-driven scientific projects is a plus.
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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