Trust & Safety Engineer

Lovable Lovable · Coding AI · Stockholm, Sweden · Engineering

This role focuses on building and shipping adaptive systems to combat monetary fraud, platform abuse, and bot attacks in real-time for a consumer-scale platform. It involves designing and implementing fraud platforms, real-time detection mechanisms (signals, features, scoring, decisioning), and bot defenses, with a strong emphasis on metrics like fraud loss rate and false-positive rate. Experience with ML scoring, real-time feature stores, and understanding attacker models is crucial.

What you'd actually do

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

Skills

Required

  • 5+ years building anti-fraud, anti-abuse, or risk systems at consumer scale
  • backend engineering (Go, Python, or TypeScript)
  • experience with rules engines
  • experience with real-time feature stores
  • experience with ML scoring
  • experience with device fingerprinting
  • experience with behavioral signals
  • understanding of precision/recall trade-offs

Nice to have

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

What the JD emphasized

  • 5+ years building anti-fraud, anti-abuse, or risk systems at consumer scale
  • disrupting sophisticated fraud campaigns and attacks
  • model an attacker, ship a counter, and measure it before they adapt

Other signals

  • real-time detection
  • ML scoring
  • adaptive systems
  • fraud prevention