```html

Requirements

5+ years building anti-fraud, anti-abuse, or risk systems at consumer scale (payments, marketplaces, fintech, or large social platforms).
Strong backend engineering - Go, Python, or TypeScript - and comfort working close to the data.
Experience with disrupting sophisticated fraud campaigns and attacks, knowledge of rules engines, real-time feature stores, ML scoring, device fingerprinting, and behavioral signals.
You think in adversaries: you can model an attacker, ship a counter, and measure it before they adapt.
Pragmatic about precision/recall trade-offs. You protect users without punishing them.

Nice to Haves

Bonus: experience with LLM-specific abuse (prompt injection at scale, generated-content fraud, credit farming) or with chargeback and payments fraud at a Stripe/Adyen/Braintree-scale merchant.

Design and ship the fraud platform that protects Lovable's payments, credits, and free tier from abuse.
Build real-time detection: signals, features, scoring, and decisioning that act in milliseconds.
Run a tight feedback loop with chargebacks, support, and trust & safety to label, learn, and re-deploy weekly.
Stand up bot defenses across signup, app generation, and publishing - without breaking legitimate users.
Own the metrics that matter: fraud loss rate, false-positive rate, attacker time-to-defeat.

Please submit your application in English - it’s our company language, so you’ll be speaking lots of it if you join
We treat all candidates equally - if you’re interested, please apply through our careers portal

```