Trust & Safety Analyst Engineer
- Salary
- ₹18–30 LPA
- Location
- Stockholm, Sweden
- Experience
- Intermediate
About company
TL;DR You'll build, analyze, and scale the systems that keep Lovable a platform the world can trust. Sitting at the intersection of data analysis, software engineering, and policy enforcement, you'll turn safety policies into automated logic that protects millions of users from harmful content, fraud, and abuse. 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 3+ years in trust & safety, platform integrity, or a related role combining data analysis and engineering at a consumer tech company. Strong technical foundation: you're comfortable writing code, querying data, and building backend workflows that run at scale. The ability to translate safety policies into automated logic, machine learning features, and scalable systems. Sharp judgment under ambiguity. You can dig into a pattern of abuse, gather context, and make