Behavior Planning Engineer

Role Description

We are looking to recruit an exceptional Robot Autonomy Engineer to build the decision-making stack that turns a goal into coordinated, reliable robot behavior in real industrial applications — what the robot should do next, in what order, and how a fleet of them shares a workspace without getting in each other’s way.

In this role you will:

• Own the autonomy stack above the controller — task planning, behavior planning, path planning and trajectory planning — from the moment work arrives to the trajectories handed off to motion control.
• Design the behavior architectures that structure long-horizon manipulation and navigation tasks, and that degrade into retry, recovery and operator handoff rather than into a stall.
• Bring principled task planning to industrial workflows: goal and precedence reasoning, task allocation, and planning under uncertainty.
• Plan and coordinate motion for multiple robots sharing an industrial facility — separation, reservation, deconfliction and deadlock‑free repositioning — so that adding a robot adds throughput.
• Integrate LLM and VLM reasoning into planning for task decomposition, subtask grounding and language‑conditioned goals, together with the verification and fallbacks that make a model’s output safe to execute on real hardware.
• Define the contract between learned policies and classical planning: what the model may decide, what the planner must guarantee, and how the two hand off mid-task.
• Interface with perception, intelligence, controls, simulation and platform software in designing functional architectures that hold up under real‑world operation.
• Hold the whole stack to measurable field performance — cycle time, success rate, intervention rate — through simulation, replay of recorded robot logs, and testing on real robots.

Must‑have:

• MS or PhD in robotics, engineering, mathematics, computer science or a related discipline.
• Real‑world experience in classical motion planning for one or more robots — search‑based, sampling‑based or optimization‑based (A*, RRT/PRM, trajectory optimization, model predictive control) — carried onto hardware rather than left in simulation.
• Real‑world experience in behavior planning: finite state machines, behavior trees or comparable behavior architectures for long‑horizon tasks, including failure detection and recovery.
• Familiarity with task planning in the classical AI planning sense (STRIPS, PDDL, HTN) or decision‑theoretic planning (MDP, POMDP), and the judgment to know when that machinery earns its complexity against a simpler reactive design.
• Proficiency in Python and C++ programming, using up‑to‑date software development practices and tooling.
• Self‑starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
• Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice‑to‑have:

• Practical experience fine‑tuning and integrating LLMs or VLMs for task planning, including grounding model output in executable, verifiable plans.
• Multi‑robot coordination at fleet scale: task allocation and assignment, traffic management, deconfliction, multi‑agent path finding.
• Experience with mobile manipulation — coordinating a mobile base and one or more arms toward a single task.
• Familiarity with ROS 2, and with fleet interface standards such as VDA
  5050.
• Familiarity with planning and kinematics libraries such as Drake, OMPL or Move It.
• Experience evaluating planners in simulation and against replayed field logs, and the regression testing that keeps a planner honest as it changes.
• A track record of carrying autonomy from working demo to sustained field operation.
• Familiarity with functional safety (FuSa) concepts.