We're looking for a mid-level ML engineer who will own the model training and fine-tuning pipeline that powers our core product features. This isn't just about getting models to work — it's about making them work well for real users. You'll run experiments on open-weight models, build the datasets that teach them what we need, and implement post-training techniques that turn raw capability into genuinely useful behavior. This role sits at the intersection of research and product development. You'll need to stay current with the latest techniques in SFT, DPO, and RLHF, but your success will be measured by whether the models you train actually make our product better. You'll design experiments that answer real questions, interpret results honestly even when they're disappointing, and iterate quickly based on what you learn. The impact is immediate and measurable. The models you train will serve millions of users, and you'll see directly how your work affects product metrics, user satisfaction, and business outcomes. This is a rare opportunity to do cutting-edge ML work while building something people actually use.