Software Engineer

We're partnering with a fast-growing (Series B) AI company that builds thedatasets and evaluation systemsfrontier AI labs use to train their models. You'd be working hand-in-hand with top research teams designing the tasks models practise on, and the scoring that decides whether they're really getting smarter. What you'd own: Design data that exposes where models fail across finance, code & enterprise workflows Build reward signals and evaluation rubrics for RLHF / RLVR training pipelines Develop frameworks to measure dataset quality and its real impact on model performance Turn ambiguous research goals into concrete, shippable systems You must have: Hands-on with modern ML/LLM workflows training, fine-tuning or evaluation Sharp instincts for data quality, edge cases and precision/recall trade-offs1–4 years shipping technical work (RLHF/RLVR or eval experience) Previous experience as a startup founder or co-founder is advantageous The details: $250 base + significant profit share + equity This is a rare chance to have direct, measurable impact on frontier AI on a small team where your work ships straight into the models defining the field.