Description
The Risk 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.
Responsibilities:
- 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:
- 8+ 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)