Physical AI Engineer (Robot Learning)

Description Beewise builds autonomous robotic beekeeping systems that operate in commercial fields. We are looking for a Physical AI Engineer to turn simulation and teleoperation data into vision-language-action (VLA) policies that can handle real beehives. This is a hands-on role with research depth, working in a small, innovative team to develop, evaluate, and deploy robot-learning solutions in real-world conditions. Location: On site at Beewise, Beit HaEmek, near Nahariya, northern Israel. Responsibilities - Fine-tune VLA and world models for bimanual, contact-rich manipulation, and optimize them for embedded edge compute. - Build physical simulation and the digital twin into scalable data sources and close the sim-to-real gap. - Follow advances in Physical AI and robot learning, and run focused experiments to assess what is worth adopting. - Shape teleoperation data collection and build tools to curate and combine real and simulated episodes. - Evaluate policies in simulation and on physical robots, and trace failures to data, models, simulation, or hardware. - Develop and implement autonomous performance evaluation methods to assess system operation, detect abnormal behavior, identify failure modes, and trigger safe shutdown and escalation when necessary. Requirements • An advanced degree in a relevant science/engineering field. • Strong foundations and hands-on experience in training and evaluating deep learning models. • Experience in Reinforcement Learning, including its application to robot learning and manipulation. • Curiosity and an open mind, with the ability to learn and explore emerging Physical AI approaches. • An innovative, research-driven approach: form independent views from papers and first principles, then test them. • Ability to scope open-ended problems and deliver working solutions independently. • Clear and honest communication of experimental results, limitations, and lessons learned. • Strong collaboration and communication skills across mechanical, embedded, and product teams. Experience training and deploying manipulation policies on a physical robot. Nice to have • Hands-on experience with imitation learning, VLA models, world models, and Reinforcement Learning. • Experience with edge deployment, including distillation, quantization, or inference optimization. • Familiarity with robotics middleware • Familiarity with robot-learning data formats and tooling. • Publications or open-source contributions in robotics or physical AI topics.