Arbetsbeskrivning

TL;DR - We’re looking for Founding ML Engineers who will shape how we fine-tune, serve, and evaluate LLMs and frontier models in production - and help define what it means to build a truly lovable AI product.

Why Lovable?

Lovable lets anyone and everyone build software with plain English. From solopreneurs to Fortune 100 teams, millions of people use Lovable to transform raw ideas into real products - fast. We are at the forefront of a foundational shift in software creation, which means you have an unprecedented opportunity to change the way the digital world works. Over 2 million people in 200+ countries already use Lovable to launch businesses, automate work, and bring their ideas to life. And we’re just getting started.

We’re a small, talent-dense team building a generation-defining company from Stockholm. We value extreme ownership, high velocity and low-ego collaboration. We seek out people who care deeply, ship fast, and are eager to make a dent in the world.

What we’re looking for

• Led or contributed to cutting-edge LLM research at top AI labs
• Deep understanding of transformer architectures, attention mechanisms, and model optimization
• Prior work on scalable AI infrastructure at a global leader (e.g. Huawei)
• Experience fine-tuning LLMs on large-scale code, language, or multimodal datasets
• Medalist at the International Math Olympiad or equivalent
• Studied computer science at a globally top-ranked university (top 5) with top grades
• Experience with code generation pipelines and developer-focused AI products
• Built fast, production-level systems with strong practices around reproducibility, monitoring, and model performance

What you’ll do

In one sentence: Train, tune, and scale frontier LLMs that power lovable products.

• Own training pipelines for LLMs, from data curation to evaluation and deployment.
• Fine-tune models on high-quality, domain-specific data (code, natural language, product usage signals).
• Work closely with product engineers to integrate models into real user-facing features.
• Build retrieval pipelines, evaluation frameworks, and experimentation tools.
• Push the limits of what’s possible with current/upcoming open models, and help define what we should train next.

Our tech stack

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

• Frontend: React for lightning-fast interfaces
• Backend: Golang and Rust for serious performance
• Cloud: Cloudflare, Fly.io, Google Cloud Run, AWS, Terraform
• DevOps & Tooling: CI/CD pipelines, observability, infra-as-code

And always on the lookout for what's next!