Data Scientist, Agent

TL;DR — You own how we measure and improve Lovable's AI agent. You build the eval systems and experiments that tell us whether a

change makes the agent better or worse, and you turn agent telemetry into the fixes that raise success rates and cut errors.

At Lovable, data scientists are not isolated model-builders; they sit close to the product, experimenting continuously with how

intelligence changes user behavior and product dynamics.

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.

• A data scientist who wants to make an AI agent measurably better, not just report on it. You own agent quality metrics and

drive them up.

• Experience or strong interest in LLM evaluation and observability: building evals, scoring outputs, tracing agent behavior, and

catching regressions.

• Strong SQL and Python, applied statistics, and experimentation. Comfortable designing A/B tests for agent changes where

outcomes are noisy.

• Instinct for what "good" looks like in agent behavior (success, error rates, task completion) and how to measure it when there

is no clean answer key.

• Build the eval systems and experiment framework that decide whether an agent change ships, like an A/B-tested rollout that

catches a change increasing errors before it reaches everyone.