Researcher, Post Training Job at Lovable (London)

Job Description

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.

Job Responsibility

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

Requirements

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?'

Nice to have

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

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Researcher, Post Training

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