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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What this interview tests

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

Common question themes

Design a detection system for a specific abuse pattern (phishing, cryptomining, token farming) on our platform

Tell me about building or fine-tuning an ML/LLM classifier for security or abuse detection

How would you detect prompt injection or jailbreaking attempts against an AI agent

Describe turning an abuse investigation finding into an automated detection rule at scale

Where do you draw the line between automated enforcement and human review, and why

Tell me about owning an abuse or security problem end-to-end, from detection to shipped fix

How candidates describe it

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