Trust & Safety Engineer

Lovable; Stockholms kommun; On-site; SEK 1,000,000 - 1,600,000; Full time

Job summary

Lovable is seeking an experienced anti-fraud platform engineer to build real-time defenses that protect payments, credits, and the free tier from abuse.

You will design signals, features, scoring, and decisioning that act in milliseconds, run a tight feedback loop with trust & safety, and deploy weekly. You think adversaries, model attackers, and measure impact to improve defenses without impacting legitimate users.

Qualifications

• 5+ years building anti-fraud or risk systems at consumer scale.
• Strong backend engineering and data proximity.
• Experience disrupting sophisticated fraud campaigns.
• Pragmatic about precision/recall; protect users.

Responsibilities

• Design and ship the fraud platform protecting Lovable's payments and credits.
• Build real-time detection: signals, features, scoring, and decisioning.
• Run a tight feedback loop with chargebacks, support, and trust & safety weekly.
• Stand up bot defenses across signup, app generation, and publishing.
• Own the metrics that matter: fraud loss rate, false positives, attacker time-to-defeat.

Skills

Backend engineering

Go

Python

TypeScript

Data proximity

Fraud/risk systems

Adversary thinking

Precision/recall

Tools

Rules engines

Real-time feature stores

ML scoring

Device fingerprinting

Behavioral signals

Job description

TL;DR Millions of people build on Lovable every month. A small fraction try to abuse it. You'll build the adaptive systems that stop monetary fraud, platform abuse, and bot attacks before they reach our users.

Why Lovable?

Lovable is the software creation platform that gives people the power to act on the problems closest to them. For decades, turning an idea into software required so much capital, technical fluency, and time that many ideas never came to life. Lovable is the counterargument: a platform for all people with ideas, ambition, and problems worth solving. From solopreneurs to small business owners to teams at companies like Adidas and Zendesk, people have built over 60 million projects on Lovable since its launch in November 2024. And we’re just getting started.

We’re building a generational company from Stockholm, with growing teams in London, Boston, New York, and San Francisco. Our team is small, talent-dense, and moving quickly, with a culture rooted in extreme ownership, high velocity, and low-ego collaboration. We look for people who care deeply, ship fast, and are eager to make a dent in the world.

Lovable is one of TIME’s 100 Most Influential Companies and has been recognized on the Forbes AI 50 and CNBC Disruptor 50, reflecting our momentum as one of Europe’s fastest-growing AI companies and one of the most ambitious places to build in this next era of software.

What we're looking for

• 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.

• 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.

What you'll do

• 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.