Applied Scientist, Post-training

Overview Teach AI how to reason — safely, transparently, and at scale. How do we move beyond pattern-matching into true machine reasoning? This Applied Scientist role puts you at the centre of that challenge — developing models that can reason, explain their logic, and make verifiable decisions across complex, high-stakes industries. You'll join a well-funded startup building domain-specific reasoning systems and agentic AI for sectors like medtech, aerospace, advanced manufacturing — where reliability and interpretability aren't optional. Your work will focus on post-training large multimodal models, applying the latest techniques in RLHF, DPO, and preference learning to make AI systems more consistent, factual, and aligned with human reasoning. You'll design the frameworks that turn raw model potential into transparent, trustworthy intelligence. You'll develop and optimise post-training pipelines, implement reward modelling for reasoning depth and factual accuracy...