Robotics Autonomy Engineer for Industrial Planning

Maven Robotics, Inc.; Santa Clara (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Maven Robotics in Santa Clara, CA is seeking a Robot Autonomy Engineer to build the decision-making stack for industrial robots. You will define planning, behavior, and coordination strategies to enable multiple robots to operate safely and efficiently in real-world facilities.

You will integrate LLMs and VLMs for task decomposition, collaborate with perception and controls teams, and push for measurable improvements in throughput, reliability, and safety across our autonomous systems.

Qualifications

• 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 and hardware deployment.
• Proficiency in Python and C++ programming, with modern software practices.

Responsibilities

• Own the autonomy stack: task planning, behavior planning, path planning, and trajectory planning from input to motion control.
• Design architectures for long-horizon manipulation and navigation with failover and recovery.
• Integrate planning with perception, controls, and simulation for robust operation in industrial environments.
• Plan and coordinate multi-robot motion in shared facilities to maximize throughput.
• Incorporate LLMs/VLMs into planning with safe verification and fallback mechanisms.
• Define contracts between learned policies and classical planners and ensure safe handoffs mid-task.
• Collaborate across teams to ensure scalable, reliable autonomous systems.

Skills

Python

C++

Motion planning

Task planning

Behavior planning

Education

MS or PhD in robotics or related field

Tools

ROS 2

VDA5050

MoveIt

Job description

Maven Robotics in Santa Clara, CA is seeking a Robot Autonomy Engineer to build the decision-making stack for industrial robots. You will define planning, behavior, and coordination strategies to enable multiple robots to operate safely and efficiently in real-world facilities.

You will integrate LLMs and VLMs for task decomposition, collaborate with perception and controls teams, and push for measurable improvements in throughput, reliability, and safety across our autonomous systems.