Staff Engineer, Fraud & Abuse

Venice.ai • Sheridan, WY

6d

About The Position

Venice is hiring a Staff Engineer, Fraud & Abuse to establish its fraud and abuse prevention function from the ground up. As the company grows, the role will own detection systems, investigate incidents, and work with engineering teams to address the weaknesses that enable abuse. The engineer will select appropriate tools and technology and work across the existing TypeScript, Rust, and Python codebase.

The role focuses on detecting and mitigating payment fraud, automated abuse, and account takeover while protecting legitimate users from unnecessary friction. It involves close collaboration with the payments team to build controls directly into payment flows, as well as ongoing measurement and tuning of detection systems to manage false positives.

Requirements

• 10 or more years of software engineering experience.
• Strong security fundamentals and the ability to think like an adversary when building systems that resist abuse.
• Comfort working across TypeScript, Rust, and Python.
• Self-directed and comfortable with ambiguity, including defining what to build and why for a new function.
• Pragmatic about tooling and able to evaluate and recommend fraud and abuse tools.
• An incident response mindset, with the ability to begin investigating issues without a runbook.

Nice To Haves

• 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 and safety or fraud detection systems at a consumer-facing platform.
• Experience with ML-based anomaly detection or behavioral analytics.

Responsibilities

• Build fraud and abuse detection systems from scratch, including rules engines, anomaly detection, and behavioral signals.
• Select the technology stack and tooling for fraud and abuse prevention.
• Investigate fraud and abuse incidents hands-on, identify what is happening, and implement fixes.
• Partner with the payments team to build fraud controls into payment flows, addressing risks such as stolen cards, chargeback abuse, and refund exploitation.
• Build signals and enforcement to counter automated abuse, including fake signups, rate-limit evasion, token farming, and API key abuse.
• Build detection and friction to prevent credential stuffing, session hijacking, and account-sharing rings while protecting the signup experience.
• Tune detection thresholds, measure impact, and reduce the risk of legitimate users being incorrectly flagged.
• Work across TypeScript, Rust, and Python to integrate detection systems with existing infrastructure.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed