Senior Software Engineer; Risk

Requirements

• 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
• 4+ years of experience in security engineering, anti‑abuse, trust & safety, or fraud detection
• Strong programming skills in Python and/or Type Script for building detection systems and automation
• Experience with SQL and data analysis at scale (Big Query, Snowflake, or similar)
• Experience building or fine‑tuning ML/LLM‑based classifiers for security or abuse detection
• Familiarity with prompt injection, jail breaking, 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
• (Desirable) Experience at a platform company dealing with user‑generated content or compute abuse (hosting providers, cloud platforms, developer tools)
• (Desirable) Background in fraud detection, payment abuse, or financial crime
• (Desirable) Familiarity with device fingerprinting, IP reputation, and email validation services
• (Desirable) Experience with CI/CD security tooling (SAST, SCA, Dependabot, Snyk)
• (Desirable) Knowledge of container security, Linux internals, or cloud infrastructure (GCP preferred)
• (Desirable) Prior work with abuse reporting pipelines, trust & safety tooling, or content moderation systems

• 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
• 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 Big Query 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, Type Script, Go, SQL
• Data:
  Big Query, Hex
• Detection tools:
  Slurper, Netwatch, Stytch (device fingerprint);
  Clear Out (email reputation)
• CI/CD Security:
  Dependabot, Snyk, SAST/SCA scanners
• Infrastructure: GCP, Kubernetes
• Collaboration:
  
  Linear, Slack, Zendesk (for abuse reports)