AI Research Engineer, Post-Training

Job summary

Responsibilities

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.

• 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…
• 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

What they're looking for

• You've personally run post-training jobs on large language models - RFT/RLVR, preference optimization, or similar.
• 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

Tech stack

• PyTorch
• JAX