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$ cat jobs/ai-prompt-engineer-linkedin3-8872d84bbaf1.json

AI Prompt Engineer

Linkedin3·Worldwide·Dublin, IE·mid
data science
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LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. This role is based in Dublin, Ireland At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. LinkedIn’s Trust Review Operations team protects our global community by ensuring AI‑driven moderation systems are safe, accurate, and reliable. As an AI Prompt Engineer (IC3), you will design, test, refine and evaluate prompts and workflows that assist in content moderation, improve detection quality, and support reviewer decision‑making. This role is ideal for someone who enjoys hands‑on experimentation, analyzing model behaviour, and partnering with Policy, Engineering, and Data Science to improve safety outcomes. What You’ll Do Prompt Design, Testing & Optimization Design and refine prompts for classification, risk detection, case summarization, and reviewer support. Run prompt experiments to diagnose issues such as hallucinations, misclassifications, bias, or inconsistent behaviour. Help maintain evaluation frameworks for accuracy, safety, and reliability. AI Case Support & Risk Mitigation Support AI‑assisted workflows in resolving medium and high‑risk cases. Identify model errors, document patterns, and recommend improvements to reduce operational risk. Contr

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