Behavior Planning Engineer

Maven Robotics; San Francisco (CA); On-site; USD 170,000 - 230,000; Full time

Job summary

Maven Robotics in San Francisco is seeking an exceptional Robot Autonomy Engineer to build the decision‑making stack that turns goals into coordinated robot behavior in real industrial applications.

You will own the autonomy stack from task planning to trajectories, design behavior architectures, and coordinate motion for multiple robots sharing an industrial facility. This role requires strong experience in Python/C++, planning, and startup‑driven execution.

Qualifications

• MS or PhD in robotics, engineering, mathematics, computer science or a related discipline.
• Real‑world experience with motion planning on hardware (search-based, sampling-based, or optimization-based).
• Real‑world experience in behavior planning (FSMs/behavior trees) for long-horizon tasks.

Responsibilities

• Own the autonomy stack from planning to trajectories, coordinating task and motion planning.
• Design behavior architectures for long-horizon manipulation and navigation with recovery/fallbacks.
• Apply planning under uncertainty, goal/precedence reasoning and task allocation.
• Coordinate multi-robot motion to maximize throughput in shared workspaces.
• Integrate LLM/VLM reasoning into planning with safety verifications for real hardware.
• Define interfaces between learned policies and classical planning.

Skills

Python programming

C++ programming

Motion planning

Behavior planning

Problem solving

Startup mindset

Education

MS or PhD in robotics or related field

Tools

ROS 2

MoveIt

Drake

OMPL

PDDL/STRIPS/HTN

VDA5050

Job description

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.

Qualifications

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 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.