Robot Autonomy Engineer
Build and operate the autonomy stack for industrial robots, spanning task, behavior, motion, and trajectory planning. The role requires hands-on hardware experience, strong Python and C++ skills, and expertise in planning architectures, recovery, and multi-robot coordination.
About the job
Responsibilities
- Own the autonomy stack above the controller, including task, behavior, path, and trajectory planning.
- Design behavior architectures for long-horizon manipulation and navigation, including retry, recovery, and operator handoff.
- Apply task planning to industrial workflows, including goal and precedence reasoning, task allocation, and planning under uncertainty.
- Coordinate motion for multiple robots in shared industrial facilities, including separation, reservation, deconfliction, and deadlock-free repositioning.
- Integrate LLM and VLM reasoning into task decomposition, subtask grounding, and language-conditioned goals with verification and safe-execution fallbacks.
- Define interfaces between learned policies and classical planning.
- Collaborate with perception, intelligence, controls, simulation, and platform software teams on functional architectures.
- Measure field performance through simulation, robot-log replay, and real-robot testing, focusing on cycle time, success rate, and intervention rate.
Requirements
- Master's or PhD in robotics, engineering, mathematics, computer science, or a related discipline.
- Real-world classical motion-planning experience transferred to hardware, using search-based, sampling-based, or optimization-based methods.
- Experience with behavior planning architectures such as finite state machines or behavior trees, including failure detection and recovery.
- Familiarity with classical AI planning, including STRIPS, PDDL, or HTN, or decision-theoretic planning, including MDP or POMDP.
- Proficiency in Python and C++.
- Strong problem identification, prioritization, planning, and execution skills.
- Ability to work effectively in a fast-paced startup environment.
Nice-to-Haves
- Experience fine-tuning and integrating LLMs or VLMs for task planning and grounding model output in executable, verifiable plans.
- Multi-robot coordination at fleet scale, including task allocation, traffic management, deconfliction, or multi-agent path finding.
- Mobile manipulation experience.
- Familiarity with ROS 2 and VDA5050.
- Familiarity with Drake, OMPL, or MoveIt.
- Experience evaluating planners in simulation and against replayed field logs, with regression testing.
- Experience carrying autonomy from working demonstrations to sustained field operation.
- Familiarity with functional safety concepts.
Skills
Python, C++, Motion Planning, Task Planning, Behavior Trees, Finite State Machines, LLMs, Vlms, Multi-Robot Coordination, Ros 2, Vda5050, Drake, Ompl, Moveit, Functional Safety
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