Senior Software Engineer, Fraud
Build and operate AI-powered fraud and abuse detection systems on Replit's agentic platform. Design LLM guardrails, ML classifiers, and automated response mechanisms to combat phishing, cryptomining, and platform exploitation.
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
- 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
Requirements
- 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
- 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
- Languages: Python, TypeScript, Go, SQL
- Data: BigQuery, Hex
- Detection tools: Slurper, Netwatch, Stytch, ClearOut
- CI/CD Security: Dependabot, Snyk, SAST/SCA scanners
- Infrastructure: GCP, Kubernetes
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