Trust & Safety Engineer
Builds adaptive anti-fraud, anti-abuse, and bot-defense systems protecting payments, credits, and platform activity. The role requires 5+ years of consumer-scale risk experience, strong backend engineering, real-time detection expertise, and adversarial thinking.
About the job
Requirements
- 5+ years building anti-fraud, anti-abuse, or risk systems at consumer scale in payments, marketplaces, fintech, or large social platforms.
- Strong backend engineering skills with Go, Python, or TypeScript, and comfort working close to data.
- Experience disrupting sophisticated fraud campaigns and attacks.
- Knowledge of rules engines, real-time feature stores, ML scoring, device fingerprinting, and behavioral signals.
- Ability to model attackers, ship countermeasures, and measure their effectiveness before attackers adapt.
- Pragmatic understanding of precision/recall trade-offs to protect users without excessively penalizing legitimate activity.
Responsibilities
- Design and ship the fraud platform protecting Lovable’s payments, credits, and free tier from abuse.
- Build real-time detection systems using signals, features, scoring, and decisioning that act in milliseconds.
- Run feedback loops with chargebacks, support, and trust and safety to label data, learn, and redeploy weekly.
- Establish bot defenses across signup, app generation, and publishing without disrupting legitimate users.
- Own key metrics including fraud loss rate, false-positive rate, and attacker time-to-defeat.
Nice-to-Haves
- Experience with LLM-specific abuse, including prompt injection at scale, generated-content fraud, or credit farming.
- Experience with chargeback and payments fraud at the scale of Stripe, Adyen, or Braintree.
Skills
Go, Python, TypeScript, Rules Engines, Feature Stores, Machine Learning, Device Fingerprinting, Behavioral Signals, Bot Detection, Fraud Detection, Chargebacks, Payments Fraud, Prompt Injection
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