The job is about keeping a fleet of humanoid robots and its data collection system running through the night. A strong candidate can set up and calibrate robots and sensors, fix hardware and software problems quickly, and keep datasets organised and reliable for machine learning work. Solid hands on experience with ROS, Linux and running automated hardware matters more than deep coding.
How it fits the AI era
The role sits at the point where AI meets physical machines: the robots generate the data that trains imitation learning models, so steady uptime directly feeds the research. Much of the monitoring, calibration and record keeping can be partly automated, but fast diagnosis of a stalled robot or a broken sensor still needs a person on site. Judgement about what counts as a clean, repeatable run stays human.
How it builds AI experience
Working nightly with a humanoid platform and its KinetIQ framework gives direct exposure to how real robot data is captured, checked and labelled for training. That builds practical understanding of data pipelines for imitation learning and of the gap between lab setups and industrial environments. It is a strong base for later work in robotics operations or data engineering for embodied AI.
Worth showing
- ROS hands on use
- Robot calibration and setup
- Fast hardware troubleshooting
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About Humanoid
Humanoid builds humanoid robots for industrial work, paired with its KinetIQ AI framework (per the company).
Tools mentioned: robotics, Python, C++, Linux, machine learning
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Robotics Engineer (Night Shift) employer: present of AI
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