Build LLM-based guardrails and abuse-detection systems defending Replit's AI-native platform

Replit's Trust & Safety team is hiring a senior engineer to build detection and automated-response systems against phishing, cryptomining, token farming, and prompt injection on an AI-native coding platform. You'll own the full abuse lifecycle from detection to enforcement to appeals, using BigQuery/Hex for investigation and building/fine-tuning LLM-based classifiers. Good prep if you want to practice designing a detection system for an adversarial, constantly-adapting attacker and defending your enforcement-automation tradeoffs.

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Designing abuse/fraud detection systems against an adaptive, adversarial attacker
Building or fine-tuning ML/LLM-based classifiers for security and abuse detection
Detecting prompt injection, jailbreaking, and other LLM-specific attack vectors
Translating investigation findings (BigQuery/Hex) into automated detection rules
Designing automated enforcement with appropriate human-in-the-loop boundaries
Owning the full abuse lifecycle: detection, investigation, enforcement, appeals