Anti-abuse Automation Engineer

Vercel Vercel · Enterprise · United States · Remote · Compliance Trust & Safety

This role focuses on building and scaling systems to protect Vercel's platform from fraud and abuse, leveraging automation and AI tools like LLMs to identify and stop bad actors. The engineer will translate insights into scalable detections, build internal tooling, and refine operational workflows.

What you'd actually do

  1. Investigate and proactively identify abuse vectors driving financial loss and platform risk (e.g., payment fraud, account abuse), translating findings into scalable detections.
  2. Build, iterate on, and operate internal fraud detection tooling, rules, and anomaly alerting systems to enable high-signal, automated enforcement.
  3. Design and continuously refine operational workflows and automation to scale fraud prevention while reducing manual investigation overhead.
  4. Partner cross-functionally with Operations, Engineering, Product, and Finance to prioritize risks and ship effective fraud mitigation solutions.
  5. Act as a key stakeholder in incident response, leading fraud investigations and developing durable mitigation and prevention strategies.

Skills

Required

  • fraud and abuse detection
  • payment fraud
  • financial fraud
  • SQL
  • data analysis
  • automation
  • scripting
  • LLMs
  • detection logic
  • risk mitigation
  • incident response

Nice to have

  • developer platforms abuse detection
  • cloud infrastructure fraud patterns
  • SaaS fraud patterns
  • edge platforms fraud patterns
  • network signals
  • device signals
  • usage patterns signals
  • IP intelligence
  • velocity analysis
  • behavioral anomalies detection

What the JD emphasized

  • 3+ years in fraud and abuse detection within Trust & Safety, with a strong focus on payment and financial fraud
  • Strong proficiency in SQL and experience navigating large, complex datasets to investigate fraud, generate insights, and build detection logic.
  • Designed and implemented automation using scripting and AI tools (LLMs, low/no-code platforms) to streamline investigations and increase enforcement throughput.
  • Built and maintained detection logic (rules, heuristics, risk signals), translating investigative insights into scalable, automated workflows.
  • Owned the iteration of enforcement and restriction strategies, optimizing for precision, coverage, and loss prevention while minimizing false positives and customer friction.
  • Partnered cross-functionally with engineering, data science, and risk teams to productionize detections and integrate ML/LLM capabilities into fraud prevention pipelines.

Other signals

  • fraud detection
  • automation
  • risk mitigation
  • LLM integration