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Staff Software Engineer, Fraud

replit· 46 open roles

Remote RemoteFullTime1 months ago
Salary
Undisclosed
Experience
Mid
Job Type
FullTime
Posted
1 months ago
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About This Role

Replit is seeking a Staff Software Engineer to join their Fraud team, focusing on detecting and preventing exploitation of their platform, including phishing, cryptomining, and LLM token farming. The role involves building and operating abuse detection systems, designing LLM guardrails, and integrating security scanners. The ideal candidate has 8+ years of experience in security engineering, anti-abuse, or fraud detection, with strong programming skills in Python and/or TypeScript. The role offers a unique opportunity to apply AI to security problems and work on adversarial challenges.

Key ResponsibilitiesAI-extracted

  • 1
    Design and implement LLM guardrails to detect abuse scenarios in AI-generated code and agent interactions
  • 2
    Build AI-powered detection systems to identify malicious patterns and automate response decisions
  • 3
    Own the full abuse response lifecycle, including detection, investigation, enforcement, and handling appeals
  • 4
    Analyze attack patterns using BigQuery and Hex, and maintain internal detection tools
  • 5
    Track abuse trends, measure detection effectiveness, and adapt defenses as attack patterns evolve

RequirementsAI-extracted

  • 8+ years of experience in security engineering, anti-abuse, trust & safety, or fraud detection
  • Strong programming skills in Python and/or TypeScript
  • Experience with SQL and data analysis at scale, such as BigQuery or Snowflake
  • Familiarity with prompt injection, jailbreaking, and other LLM-specific attack vectors
  • Clear communication skills for working across Security, Support, Legal, and Engineering teams

Perks & BenefitsAI-extracted

Competitive Salary & Equity
401(k) Program with a 4% match
Health, Dental, Vision and Life Insurance
Paid Parental, Medical, Caregiver Leave
Flexible Time Off (FTO) + Holidays
Typically: Opportunities for professional growth and development in a rapidly evolving field

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