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.

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

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.

You build systems and agents that produce this insight continuously, rather than one-off analyses.

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.

Entrepreneurial. Thrives in ambiguity, and works closely with the engineers building the agent.

What you'll do

Define and own the metrics for agent quality: success, completion, error rates, and the behaviors that drive them.

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.

Turn agent traces and telemetry into concrete fixes, working directly with the agent engineering team.

Build the tooling and agents that produce these evaluations continuously as the agent evolves.

Set the bar for how we judge agent behavior where there is no answer key to check against.

Our tech stack

We're building with tools that both humans and AI love:

Languages: SQL and Python

LLM evaluation & observability: Braintrust, OTEL tracing, many LLM providers

Warehouse & events: BigQuery, PubSub

Analytics & product: Hex, Lovable Apps

Experimentation: A/B and growth testing

Cloud: GCP

And always on the lookout for what's next.

We treat all candidates equally.

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