Anti-Abuse Automation Engineer

Vercel gives developers the tools and cloud infrastructure to build, scale, and secure a faster, more personalized web. As the team behind v0, Next.js, and AI SDK, Vercel helps customers like Ramp, Supreme, PayPal, and Under Armour build for the AI-native web.

Our mission is to enable the world to ship the best products. That starts with creating a place where everyone can do their best work. Whether you're building on our platform, supporting our customers, or shaping our story: You can just ship things.

We’re looking for an Anti-Abuse Automation Engineer to help build and scale the systems that protect Vercel’s platform from fraud and abuse. Our Trust & Safety Engineering team sits at the intersection of product, infrastructure, and risk, tackling challenges across financial fraud, phishing, malware, CSAM, platform abuse, and IP and DMCA enforcement.

In this role, you will not just investigate abuse, you will turn insights into systems. You will design detection logic, build automation, and develop workflows that proactively identify and stop bad actors at scale. Working closely with Operations, Product, Finance, and Engineering, you will play a key role in shaping how Vercel prevents fraud, reduces risk, and protects developers and businesses on our platform.

If you’re based within a pre-determined commuting distance of one of our offices (SF, NY, London, or Berlin), the role includes in-office anchor days on Monday, Tuesday, and Friday. If you're located beyond that distance, the role is fully remote. For location-specific details, please connect with our recruiting team.

• →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.

• 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.