RESEARCHER, POST-TRAINING
ABOUT THE COMPANY
We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-site
ABOUT THE ROLE
You'll lead our work on model post-training: supervised fine-tuning, preference data, reinforcement learning from human and AI feedback, reward modeling, and the evaluation suites that tell us what's actually working. You'll own a research area that meaningfully shapes our model behavior and capability.
This is a hands-on senior research role. You'll set direction, run experiments, and ship into production. You'll partner with the data, infrastructure, and engineering teams to make the post-training pipeline reliable and fast: improvements there compound into every model we ship.
WHAT YOU'LL DO
Lead post-training research: SFT, RLHF/RLAIF, RLVR, DPO and successor methods, reward modeling, preference data design
Design and curate the data that goes into post-training (from sourcing, to filtering, to quality assessment)
