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Open nowFirst seen 1 hour ago

Physical AI Engineer (Robot Learning)

Beewise20 open roles

Where
Beit HaEmek (Northern District), IL
Work mode
On site
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Your applicationOpen nowPhysical AI Engineer (Robot Learning)Beewise · Beit HaEmek (Northern District), IL

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The clock on this job

Early applications get read.

13.1% of postings close within 7 days. Measured by our own scanner across the market. Beewise postings stay open a median of 6 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.8%30 days
  6. 59.0%60 days
This job: first seen 1 hour ago

Beewise median: 6 days open

The posting

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.
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