EU remote
Analytical Chemistry Specialist - Freelance AI Trainer Project
About this role
This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an Analytical Chemistry Specialist – Freelance AI Trainer Project based in Switzerland. Are you an analytical chemistry expert interested in contributing to the next generation of artificial intelligence? This freelance opportunity allows you to apply your scientific expertise to train and evaluate advanced AI models designed to support scientific discovery, education, and laboratory research.
You will assess AI-generated responses to complex analytical chemistry questions, identify inaccuracies, and strengthen scientific reasoning across a wide range of applications. Your work will help improve the accuracy, reliability, and practical relevance of AI-generated scientific knowledge. Working remotely and independently, you will contribute specialized insights to the development of more capable and trustworthy AI systems.
This role is ideal for experienced chemistry professionals who enjoy solving complex problems, communicating scientific concepts, and exploring the intersection of chemistry and emerging technology. Accountabilities Evaluate AI-generated scientific content: Review responses to theoretical and practical analytical chemistry questions, assessing their factual accuracy, logical consistency, scientific rigor, and relevance to real-world laboratory applications.
Apply specialized analytical chemistry knowledge: Develop and evaluate scenarios involving spectroscopy, chromatography, electrochemical analysis, quantitative chemical analysis, environmental testing, pharmaceutical analysis, and analytical method development. Assess advanced analytical techniques: Challenge AI models on mass spectrometry, high-performance liquid chromatography (HPLC), gas chromatography (GC), nuclear magnetic resonance (NMR) interpretation, trace analysis, titrimetric methods, and related instrumentation.
Identify and document model weaknesses: Record reproducible errors, reasoning gaps, inaccurate interpretations, and other failure patterns to support systematic improvements in model performance. Contribute to AI training and evaluation: Provide detailed scientific feedback, propose corrections, and recommend improvements to prompts, evaluation criteria, and testing methodologies. Support scientific quality assurance: Apply principles of method validation, quality assurance and quality control (QA/QC), and analytical data interpretation to evaluate the reliability of AI-generated outputs.