Original listing text, shown exactly as published by the company.
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.
Nice to have
- Experience at a platform company dealing with user-generated content or compute abuse (hosting providers, cloud platforms, developer tools)
- Background in fraud detection, payment abuse, or financial crime
- Familiarity with device fingerprinting, IP reputation, and email validation services
- Experience with CI/CD security tooling (SAST, SCA, Dependabot, Snyk)
- Knowledge of container security, Linux internals, or cloud infrastructure (GCP preferred)
- Prior work with abuse reporting pipelines, trust & safety tooling, or content moderation systems
Tools + Tech Stack for this role
- Languages: Python, TypeScript, Go, SQL
- Data: BigQuery, Hex
- Detection tools: Slurper, Netwatch, Stytch (device fingerprint); ClearOut (email reputation)
- CI/CD Security: Dependabot, Snyk, SAST/SCA scanners
- Infrastructure: GCP, Kubernetes
- Collaboration: Linear, Slack, Zendesk (for abuse reports)
This role may not be a fit if
- You prefer deep security research over building operational detection systems
- You want to focus on vulnerability management, pentesting, or bug bounty triage (that's our Security team)
- You're looking for a role with predictable, well-defined problems rather than constantly adapting to adversarial behavior
- You prefer working in isolation rather than partnering closely with Support, Legal, and cross-functional teams
- You're uncomfortable making enforcement decisions that affect real users
This is a full-time role that can be held from our Foster City, CA office. The role has an in-office requirement of Monday, Wednesday, and Friday.
