About this role
Summary: Beewise seeks a Physical AI Engineer to develop and deploy robot-learning solutions for autonomous beekeeping systems. The role involves fine-tuning VLA models, building physical simulations, and evaluating policies in real-world conditions.
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.
• 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.