Real-world training data for frontier AI labs and robotics. Backed by Y Combinator.
About the role
You build the scientific program of Hub Labs with Guillaume. You start from what actually goes wrong when robots run, turn it into falsifiable questions and answer them with rigorous experiments. This role keeps real research capacity: time to think, design studies and publish, alongside hands-on work on physical robots. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- A PhD in machine learning, robotics or a related field, followed by several years of research in academia or an industrial lab
- Research depth in robot learning: publications at venues such as CoRL, RSS, ICRA, NeurIPS, ICML, ICLR or CVPR
- Practical robot-learning evidence: policies you trained and evaluated on real robots, not only in simulation
- Experience in areas such as imitation learning, VLAs, world models, RL, human-to-robot transfer or learning from video
- A track record of experimental rigour and of working closely with engineers
- Fluent English, written and spoken
More about this role
Hub (Y Combinator 2026) collects real-world human and robot data to train robots, and works with some of the most recognised robotics and AI labs in the United States. Today Hub specialises in egocentric data collected at a scale and speed no lab could reach on its own: every contributor is identity-verified (KYC) and every contribution goes through several layers of quality control. Scalable QA of egocentric data, run by our Refinery team, remains a core company priority.
Hub Labs, headquartered in Toulouse, is where Hub turns that data into working robot behaviour. Within Hub Labs, the Brain team, led by Guillaume, works on robot learning across humanoids and other types of embodied machines.
You build the scientific program of Hub Labs with Guillaume. You start from what actually goes wrong when robots run, turn it into falsifiable questions and answer them with rigorous experiments. This role keeps real research capacity: time to think, design studies and publish, alongside hands-on work on physical robots.
- Formulate falsifiable research questions from robot failures observed in trials and deployments.
- Design rigorous experiments: hypotheses, controls, held-out evaluation...
Browse similar: AI jobs · AI startup jobs · Startup jobs · San Francisco Bay Area