SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Lovable is a platform that enables millions of users to build software applications without traditional coding barriers. The company is experiencing rapid growth with hundreds of millions of monthly visits to Lovable-built applications and a compounding enterprise customer base.
As a Trust & Safety Engineer, you will design and build the fraud detection and abuse prevention systems that protect Lovable's payments infrastructure, credits system, and free tier from sophisticated attacks. This is a high-impact role where you'll own the full lifecycle of fraud prevention—from real-time signal detection and feature engineering to ML-based scoring and decisioning systems that operate at millisecond latencies.
Key responsibilities include:
- Architecting and deploying a comprehensive fraud platform protecting payments, credits, and platform access
- Building real-time detection systems using signals, feature stores, and ML scoring that make decisions in milliseconds
- Establishing tight feedback loops with chargebacks, support, and trust & safety teams to continuously label data, train models, and redeploy defenses weekly
- Implementing bot defenses across signup, app generation, and publishing workflows while maintaining frictionless experiences for legitimate users
- Owning critical metrics: fraud loss rate, false-positive rate, and attacker time-to-defeat
You'll work with a small, talent-dense team in Stockholm that values extreme ownership, high velocity, and low-ego collaboration. The role requires deep backend engineering expertise (Go, Python, or TypeScript) and hands-on comfort working directly with data. You should have 5+ years building anti-fraud, anti-abuse, or risk systems at consumer scale (payments, marketplaces, fintech, or large social platforms). Experience with rules engines, real-time feature stores, device fingerprinting, behavioral signals, and chargeback/payments fraud is essential. Bonus experience includes LLM-specific abuse patterns (prompt injection, generated-content fraud, credit farming) or merchant-scale fraud at companies like Stripe, Adyen, or Braintree.
