Anti-Abuse Automation Engineer

Vercel is a company that provides developers with tools and cloud infrastructure for building a faster, more personalized web. They are seeking an Anti-Abuse Automation Engineer to develop systems that protect their platform from fraud and abuse, working closely with various teams to enhance fraud prevention measures.

Responsibilities:

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

Requirements:

• 3+ years in fraud and abuse detection within Trust & Safety, with a strong focus on payment and financial fraud (e.g., account takeovers, payment abuse, chargebacks, promo abuse)
• 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
• Experience detecting and mitigating abuse in developer platforms (e.g., API abuse, free tier exploitation, bot-driven signup or usage abuse)
• Familiarity with fraud patterns in cloud infrastructure, SaaS, or edge platforms (e.g., resource abuse, billing evasion, account farming)
• Experience leveraging signals from network, device, and usage patterns (IP intelligence, velocity, behavioral anomalies) to detect sophisticated abuse
• Comfort working in fast-moving, product-led environments, rapidly shipping detections and iterating alongside engineering teams