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
- 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 VDA5050
- Familiarity with planning and kinematics libraries such as Drake, OMPL or MoveIt
- 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
Equal Opportunity
Maven Robotics is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.