Computer Sciences - Graduates - AI Training We’re looking for Computer Science Specialists to join our Expert Network to help train and evaluate cutting‑edge AI models. If you have a background in CS research or technical analysis, we’ll send you a quick 10‑ to 15‑minute test to assess your skills. If successful, you’ll be invited to join Prolific as a participant, where you’ll get paid to help AI understand and summarize complex scientific data. Researchers looking for your skills tend to pay up to $60/hr, depending on skills and experience level. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter. What you’ll bring Educational Background: at minimum, a BSc (Bachelor of Science) in Computer Science or a closely related technical field. Technical Literacy: ability to interpret research papers, understand complex algorithms, and review code logic. Analytical Mindset: high level of cognitive competency with a sharp eye for technical hallucinations or logical flaws. Professional Verification: a valid LinkedIn profile to verify your degree and background during the screening process. A PayPal account to receive payment from our clients What you’ll be doing in the role AI Evaluation & Ranking: comparing multiple AI-generated responses to technical prompts and ranking them based on accuracy, logic, and safety. Scientific Review: reviewing CS research papers alongside AI-generated summaries and graphical abstracts to ensure scientific integrity. Fact-Checking: identifying inaccuracies where the AI has misinterpreted technical data, formulas, or research findings. RLHF (Reinforcement Learning from Human Feedback): providing the human "ground truth" to help models align with professional standards in software engineering and data science. Code & Logic Verification: auditing AI-generated code snippets or architectural diagrams for structural and functional correctness. Key Technologies Generative AI & LLMs: training the next generation of technical and reasoning models. Technical Documentation: working with research papers, code repositories, and data visualizations. Verification Frameworks: using structured evaluation rubrics to audit AI performance.
