Computer Sciences - Graduates - AI Training
About Prolific: Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.
The role
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 paying for your skills may offer up to $60/hr, depending on expertise. 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.
* 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.
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