Senior Software Engineer, Replit

• 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
• We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds

Desirable

• 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
• 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
• 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)