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 complex datasets related to medicinal chemistry using advanced bioinformatics methodologies.
Provide detailed scientific input and content to support the development and training of AI models.
Curate, annotate, and validate datasets relevant to drug discovery and molecular analysis.
Evaluate and synthesize findings from biological, chemical, and clinical data sources.
Offer subject matter expertise on experimental design and data interpretation within medicinal chemistry.
Assess AI-generated outputs for scientific accuracy, relevance, and reliability.
Deliver comprehensive written feedback and actionable recommendations for model improvement.
Preferred skills
Advanced degree (e.g., PhD or MSc) in Bioinformatics, Computational Biology, Medicinal Chemistry, or a related discipline.
In-depth knowledge of medicinal chemistry concepts, including structure-activity relationships and drug design principles.
Demonstrated experience in handling and interpreting large-scale omics or cheminformatics datasets.
Familiarity with software tools, databases, and programming languages commonly used in bioinformatics (e.g., Python, R, RDKit, KNIME).
Strong scientific communication skills, with the ability to clearly articulate complex ideas and technical concepts.
Proven track record of contributing to research projects at the intersection of biology, chemistry, and data science.
Experience collaborating in multidisciplinary or remote project environments is advantageous.
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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