Researcher, Post Training

Lovable lets over 2 million people build software using plain language, and the models behind it need to be exceptional. We're hiring an engineer who has gotten their hands dirty with post‑training at scale and wants to do it again for one of the fastest‑growing AI products in the world.

You will own our full post‑training pipeline: translating the latest research into production training recipes, adapting them for code generation and agent workloads, and putting improved models in front of users fast. The goal is to get promising research into production within days or weeks, not months. This isn't an academic research position – you'll spend as much time in production infrastructure as in training configs, and your success is measured by what ships.

Why Lovable? Lovable lets anyone and everyone build software with any language. 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 more than 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

• You've personally run post‑training jobs on large language models – RFT, RLVR, preference optimization, or similar – not just called APIs or written prompts, but actually trained and iterated on models.
• You can write solid production code. The systems you build need to run reliably, not just produce interesting research artifacts.
• You're fluent in at least one major ML framework (PyTorch, JAX) and comfortable working with distributed training setups and GPU clusters.
• You understand the math behind preference optimization, reward modeling, and alignment techniques, and can reason about when each approach fits.
• You've built or significantly contributed to evaluation systems that capture real‑world quality, not just benchmark scores.
• You can trace a model‑quality regression from user‑facing symptoms back through serving, inference, and training – and you enjoy doing it.
• You want to ship. Research taste matters, but at Lovable the question is always 'how fast can we get this to users?'.

Preferred

• You've worked on code generation or agentic use cases specifically.
• You've put post‑trained models into the hands of real users and seen how they hold up at scale.
• You've owned the full loop: curating data, running training, evaluating results, deploying, and monitoring in production.
• You have a habit of reading a paper on Monday and having a prototype running by Friday.
• You've experimented with speculative decoding or similar techniques to improve model efficiency.
• You have strong views on evaluation methodology and have built evals that actually predict user satisfaction.
• You've published or contributed meaningfully to the open‑source ML ecosystem.

What You’ll Do

• Own the full lifecycle of Lovable's post‑training pipeline – from data curation and training runs through evaluation and deployment.
• Apply and adapt reinforcement learning, preference optimization, and supervised fine‑tuning methods to make our models better at generating code, reasoning about user intent, and acting as reliable agents.
• Build the evaluation and experimentation infrastructure that tells us whether a model change actually helps users – covering helpfulness, safety, latency, and reliability.
• Develop and operate the production systems that run training jobs at scale, including GPU orchestration and data pipelines.
• Work across team boundaries with our agent, product, and infrastructure engineers to turn model gains into product improvements users can feel.
• Investigate and resolve failures end‑to‑end – whether the root cause is in a training recipe, a data issue, or a serving regression.
• Read papers, run experiments, and move fast: the goal to get promising research into production within days or weeks, not months.