Teams at RoboForce

Lead engineering of the full embodied systems stack for real-world robots, owning onboard inference, data collection, teleoperation, and on-robot RL systems. Set architecture and technical direction, deliver reliable real-time deployments, close the loop between data, models and actions, and mentor engineers to scale embodied AI in industrial environments.

The role involves building and maintaining AI infrastructure, optimizing training performance, and developing tools for robot training and data workflows.

Lead and own end-to-end operational execution of embodied AI programs for robot learning. Build and manage a 20–50 person team across data collection, annotation, and teleoperation; define SOPs, KPIs, fleet readiness, vendor management, and evaluation workflows; partner with researchers to translate priorities into scalable operational plans.