Senior Domain Expert – Agriculture, Food & Wine Systems (AI & Technical Evaluation)

Core Responsibilities & Competencies1. Domain Expertise & Visual InterpretationInterpret, analyze, and validate complex scientific imagery, technical charts, graphs, patent drawings, technical illustrations, and geographic/topographic maps within the agricultural and food sciences.Apply specialized subject-matter knowledge across the following core areas:Soil Physics & Texture: Soil mechanics, water retention, and structural dynamics.Plant Pathology & Disease Assessment (Bal): Pathogen monitoring, diagnostic protocols, and crop health evaluation.Viticulture & Wine Regions: Grapevine physiology, terroir, and regional management practices.Fisheries Stock Assessment: Ecological modeling and evaluation using Kobe plots.Food Process Engineering & Thermal Processing: Manufacturing optimization, food safety, and thermal processing dynamics.Forestry & Silviculture: Stand dynamics, growth modeling, and yield predictions using stand-density diagrams.Plant Breeding & Field Trials: Experimental design, phenotypic evaluation, and field trial execution.2. Analytical Thinking & Problem SolvingApply critical thinking and rigorous evidence evaluation to solve complex, unstructured agricultural and food science problems.Validate technical solutions, datasets, and methodologies against established industry standards, scientific literature, and regulatory benchmarks.3. Problem Design & Technical CommunicationDevelop challenging, high-fidelity tasks, technical questions, rubrics, evaluation templates, and gold-standard reference solutions.Document and explain highly complex scientific concepts clearly and concisely for multidisciplinary teams and technical stakeholders.4. Evaluation, Quality & FairnessConduct meticulous technical reviews, error detection, and accuracy validations on scientific datasets or AI-generated outputs.Calibrate quality standards and design fair, robust assessments that accurately measure genuine domain expertise.5. Research, Digital Literacy & Professional PracticeConduct thorough academic and technical research, including source validation, licensing, and data provenance awareness.Utilize modern digital tools and participate in AI/LLM evaluation and RLHF (Reinforcement Learning from Human Feedback) workflows.Demonstrate meticulous attention to detail, strong collaboration skills, and adaptability in a fast-paced environment.Qualifications & RequirementsEducation: Advanced degree (M.Sc., Ph.D., or equivalent practical experience) in Agronomy, Food Science, Viticulture, Forestry, Soil Science, Environmental Science, or a related technical discipline.Experience: 3+ years of professional or research experience within one or more of the specialized sub-domains listed above.Technical Proficiency:Strong foundation in data analysis, scientific research methodologies, and technical documentation.Familiarity with AI model evaluation, data annotation, or prompt engineering frameworks is a strong plus.Communication: Exceptional written and verbal communication skills with the ability to articulate complex technical data clearly.What We OfferCompetitive compensation package.Flexible working arrangements (Remote-friendly).Opportunity to shape the future of AI technology by training state-of-the-art models with expert-level scientific knowledge.