About the role
Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation.
About
the role The Fraud team is the front line defending Replit's platform from exploitation. We detect and shut down phishing deployments, prevent cryptomining on free-tier infrastructure, stop LLM token farming, and keep bad actors from weaponizing the platform against our users. This is adversarial work: attackers adapt constantly, and we build the detection systems, heuristics, and automated responses that stay ahead of them. What makes this role unique is the AI-native nature of Replit's platform. You'll work on problems that barely exist elsewhere: building guardrails for AI-generated code, detecting prompt injection attacks at scale, and using LLMs as a defensive tool against abuse. If you want hands-on experience applying AI to security problems, this is one of the few places you can do it in production with real attackers. You'll own problems end-to-end, from identifying emerging abuse patterns to shipping the systems that stop them at scale. In this role you will… Design and implement LLM guardrails that detect abuse scenarios in AI-generated code and agent interactions Build AI-powered detection systems that use LLMs to identify malicious patterns, classify threats, and automate response decisions Build and operate abuse detection systems that identify phishing, cryptomining, account takeover, and financial fraud across millions of daily user actions Design automated response mechanisms that enforce platform policies without manual intervention Own the full abuse response lifecycle: detection, investigation, enforcement, and handling appeals alongside Support and Legal Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection rules Maintain and extend internal detection tools (Slurper, Netwatch) that continuously monitor user activity Integrate and tune security scanners (SAST, SCA) in CI pipelines with tight performance SLAs Track abuse trends, measure detection effectiveness, and adapt defenses as attack patterns evolve Required skills and experience: 4+ years of experience in security engineering, anti-abuse, trust & safety, or fraud detection Strong programming skills in Python and/or TypeScript for building detection systems and automation Experience with SQL and data analysis at scale (BigQuery, Snowflake, or similar) Experience building or fine-tuning ML/LLM-based classifiers for security or abuse detection Familiarity with prompt injection, jailbreaking, and other LLM-specific attack vectors Ability to investigate complex abuse patterns and translate findings into automated defenses Familiarity with common attack patterns: phishing infrastructure, account takeover, credential stuffing, resource abuse Clear communication skills for working across Security, Support, Legal, and Engineering teams.
