Robot Autonomy Engineer

About the role

About Maven

Maven Robotics designs, builds, and deploys intelligent robotics for logistics and manufacturing. Maven is building the first general-purpose robotics system for industrial work. Our mission is to activate autonomous labor at scale and build toward infinitely elastic industrial capacity. We’re a team of engineers, researchers, and operators who have spent our careers working on the hardest problems in AI, robotics, engineering, and autonomous systems. We work at the intersection of advanced research and real-world engineering, turning ideas into prototypes and deployed systems.

At Maven, we build around the realities of industrial work. The robots are designed from first principles for complex physical environments and rigorous commercial standards. They master real tasks that deliver immediate impact, industrial-grade reliability, and intelligence that compounds at scale. We’re growing an ambitious team of people who think in systems, move quickly, and are builders at heart.

About the Role

As a Robot Autonomy Engineer at Maven Robotics, you will build the decision-making stack that enables robots to translate goals into coordinated, reliable actions in real-world industrial environments. You’ll develop systems that determine what robots should do, when and in what sequence, while ensuring multiple robots can operate efficiently and safely in shared workspaces.

What You’ll Do

- 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

What We’re Looking For

- MS or PhD in robotics, engineering, mathematics, computer science or a related discipline