Real-world training data for frontier AI labs and robotics. Backed by Y Combinator.
About the role
This is a hands-on physical robotics role. You take existing robot policies, make them run on our robots and data, and measure honestly what works. Your first goal is a reproducible baseline on real hardware, followed by one measured data or model experiment that tells us something we did not know. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Hands-on experience training or adapting robot-learning policies (for example imitation learning, diffusion or transformer policies, VLAs, or RL), not only reading about them
- Experimental discipline: clean splits, controlled comparisons, reproducible runs and honest reporting
- Experience running learned policies on real robots: sensors, calibration, latency and the failures that only show up in the physical world
- Excellent Python and PyTorch, comfortable with Linux, GPUs and robotics software
- Experience with humanoids, manipulators, mobile robots or other embodied systems
- 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.
This is a hands-on physical robotics role. You take existing robot policies, make them run on our robots and data, and measure honestly what works. Your first goal is a reproducible baseline on real hardware, followed by one measured data or model experiment that tells us something we did not know.
- Reproduce accessible existing policies and get them running on our robots.
- Adapt observation and action spaces to our embodiments and sensor setups.
- Build versioned datasets with clean...
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