Staff Engineer, Fraud & Abuse

About Us

Venice is the world’s leading consumer AI company built on principles of privacy, free speech, and user sovereignty.

We’re building the Port City of AI, in which millions of individuals and AI agents gather, interact, and access sophisticated AI resources on a private and permissive foundation.

Joining Venice means joining a team of unorthodox builders who believe in moving quickly, delivering a beautiful mass-market consumer product that doesn’t spy on people or censor their ideas and questions., If you’re energized by big ideas, entrepreneurial spirit, individual empowerment, and the opportunity to help shape a fast-growing company in the world’s hottest industry from the ground up, please reach out.

Why We're Hiring

Venice is growing fast, and so is the attack surface. Fake account creation at scale, credential stuffing, payment fraud, free-tier farming, API abuse. We don’t have a dedicated fraud and abuse prevention function today, and we need one. This is a build-from-zero role. You’ll own the detection systems, investigate incidents, and work with engineering to close the gaps that make abuse possible in the first place.

What you’ll do

• Build detection systems from scratch. Rules engines, anomaly detection, behavioral signals. You’ll decide what stack and tooling we use. We’ve got TypeScript, Rust, and Python in the codebase today, and you’ll work across all three as needed.

• Investigate incidents hands-on. When a fraud spike hits or a new abuse pattern surfaces, you’re the one digging in, figuring out what’s happening, and shipping a fix. Not handing it off, owning it.

• Partner with the payments team. As we scale monetization, payment fraud (stolen cards, chargeback abuse, refund exploitation) becomes a core risk. You’ll work directly with the payments team to build fraud controls into the payment flow, not bolt them on after.

• Stop automated abuse. Bot-driven fake signups, rate limit evasion, token farming, API key abuse. Build the signals and enforcement that make large-scale automation expensive and unattractive.

• Prevent account takeover. Credential stuffing, session hijacking, account sharing rings. Build detection and friction that protects real users without wrecking the signup experience.

• Iterate with data. Every detection system has false positives. You’ll tune thresholds, measure impact, and make sure legitimate users aren’t caught in the net.

What we’re looking for

• 10+ years of software engineering experience , with strong security fundamentals. You need to think like an adversary and care about building systems that resist abuse.

• Comfortable across TypeScript, Rust, and Python. You won’t be boxed into one language. Our codebase spans all three, and the detection systems you build will need to integrate with existing infrastructure.

• Self-directed and comfortable with ambiguity. This is a new function. There’s no playbook to follow, no existing system to maintain. You’ll define what we build and why.

• Pragmatic about tooling. You’ll recommend the fraud and abuse stack. We’re not wedded to a specific vendor. If you know the right tools, great. If not, you’ll evaluate and choose.

• Incident response mindset. When something breaks, you dig in. You don’t need a runbook to start investigating.

Nice to have

• Experience with payment fraud detection, chargeback management, or billing abuse

• Familiarity with Cloudflare, WAF rules, bot management, or similar edge-layer controls

• Prior experience building trust & safety or fraud detection systems at a consumer-facing platform

• Experience with ML-based anomaly detection or behavioral analytics