Domain Expert

We are looking for researchers, academics, and recognized domain experts with deep expertise across STEM and broader knowledge-worker domains to contribute their subject-matter expertise to the training and improvement of Large Language Models (LLMs) and other AI systems.This is an opportunity to work at the intersection of deep domain expertise and advanced AI, helping improve how AI models understand, reason about, and respond to complex domain-specific problems.The ideal candidate brings strong academic or industry expertise and is able to translate that knowledge into high-quality training content, examples, explanations, reasoning processes, and expert feedback that can be used to improve AI models.DomainsWhat You Will DoDepending on your area of expertise, you may contribute to:Develop high-quality domain-specific training data for LLMs and AI models.Create complex, realistic and representative questions, problems, scenarios, examples and solutions within your area of expertise.Provide detailed explanations and reasoning to help models learn how experts approach complex problems.Review and improve AI-generated content for accuracy, relevance, depth, reasoning quality and domain appropriateness.Help develop examples that require multi-step reasoning, domain judgment and nuanced understanding.Support the development and refinement of datasets used for pre-training, post-training, instruction tuning and model evaluation.Contribute to specialized AI projects involving RLHF, preference data, model alignment and expert feedback, where relevant.Work with AI researchers, project teams and other domain experts to translate complex subject knowledge into formats that AI systems can learn from.Areas of ExpertiseWe are particularly interested in experts across:STEMComputer Science & Software EngineeringData Science & AI/MLMathematics & StatisticsPhysicsChemistryBiology & Life SciencesEngineering – Mechanical, Electrical, Civil, Chemical, etc.Environmental SciencesBiotechnologyMaterials ScienceOther Knowledge DomainsEconomicsFinance & AccountingBusiness & ManagementLawPsychologySociologyEducationResearch & AcademiaHealthcare-related knowledge domainsLinguisticsCommunicationsSupply Chain & OperationsMarketingOther specialized professional domainsWho We Are Looking ForWe welcome applications from:PhD researchers and postdoctoral researchersUniversity professors, lecturers and research scientistsExperienced industry professionals and technical specialistsRecognized subject-matter expertsEngineers, scientists and researchersProfessionals with significant experience in specialized or emerging fieldsExperts with previous experience contributing to AI/LLM training or evaluation programsWhat Makes You a Strong FitYou should have:Deep subject-matter expertise demonstrated through academic qualifications, research, publications, professional experience or recognized industry contribution.Strong analytical and critical-thinking ability.Ability to explain complex concepts clearly, accurately and systematically.Attention to detail and commitment to factual accuracy.Comfort working with AI-generated content and critically evaluating model outputs.Particularly Valuable ExperienceExperience in any of the following will be an advantage:LLM or generative-AI trainingAI/ML researchTraining-data creation or curationInstruction tuning / fine-tuningRLHF / RLAIFPreference-data generationLLM response evaluationPrompt engineeringAI model benchmarkingRed teaming or adversarial testingAI safety or responsible AIWorking with AI research labs or technology companiesEngagementEngagements may be structured as project-based or flexible expert assignments, depending on the nature and duration of the program.Experts may contribute remotely and work on projects aligned with their specific domain expertise.Why JoinThis is an opportunity to contribute directly to the development of the next generation of AI systems by bringing something AI cannot generate reliably on its own: deep human expertise, judgment and domain understanding.Your contribution can help AI models become more accurate, capable, reliable and useful in specialized areas of knowledge.