AI Research Engineer, Post-Training

TL;DR:

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'll 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.

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.

• 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 can trace a model quality regression from user-facing symptoms back through serving, inference, and training - and you

enjoy doing it

• 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

• 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 is to get promising research into production within days or weeks, not

months