Computer Sciences - Graduates - AI Training - Omaha,
Listed on 2026-05-26
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IT/Tech
AI Engineer, Data Scientist
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.
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 Linked In profile to verify your degree and background during the screening process.
- Pay Pal account: to receive payment from our clients.
- 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.
- 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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