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About the Role

Build the decision-making stack that turns a goal into coordinated, reliable robot behavior in real industrial applications, defining what the robot should do next, in what order, and how a fleet shares a workspace without interference.

Responsibilities

• Own the autonomy stack above the controller, including task planning, behavior planning, path planning, and trajectory planning.
• Design behavior architectures for long-horizon manipulation and navigation tasks, incorporating retry, recovery, and operator handoff mechanisms.
• Implement principled task planning for industrial workflows, including goal and precedence reasoning, task allocation, and planning under uncertainty.
• Plan and coordinate motion for multiple robots in an industrial facility, ensuring separation, reservation, deconfliction, and deadlock-free repositioning.
• Integrate LLM and VLM reasoning into planning for task decomposition, subtask grounding, and language-conditioned goals, with verification and fallbacks for safe execution.
• Define the contract between learned policies and classical planning, specifying model decision boundaries and planner guarantees.
• Interface with perception, intelligence, controls, simulation, and platform software to design robust functional architectures.
• Ensure measurable field performance of the entire stack, including cycle time, success rate, and intervention rate, through simulation, log replay, and real robot testing.

Requirements

• 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 (e.g., A*, RRT/PRM, trajectory optimization, MPC) implemented on hardware.
• Real-world experience in behavior planning using finite state machines, behavior trees, or comparable architectures for long-horizon tasks, including failure detection and recovery.
• Familiarity with classical AI planning (STRIPS, PDDL, HTN) or decision-theoretic planning (MDP, POMDP).
• Proficiency in Python and C++ programming with up-to-date software development practices and tooling.
• Self-starter attitude with strong problem identification, prioritization, planning, and execution skills.
• Enthusiasm for a fast-paced startup environment and willingness to support the team on various topics.

Skills

• Task planning
• Behavior planning
• Path planning
• Trajectory planning
• LLM integration
• VLM integration
• Classical AI planning
• Decision-theoretic planning
• Python
• C++
• Multi-robot coordination
• Mobile manipulation
• ROS 2
• VDA5050
• Drake
• OMPL
• MoveIt
• Simulation
• Regression testing
• Functional safety (FuSa)

Location

• Industrial applications

Work Type

• Full-time

Experience Level

• MS or PhD
• Real-world experience

Education Level

• MS or PhD