Robotics Engineer, Motion Planning

At Contoro Robotics, we're on a mission to solve labor challenges through advanced robotic solutions. Headquartered in Austin, TX, our fast-growing startup is transforming the supply chain industry with our flagship warehouse automation technology. Our team is made up of top-tier experts in robotics, AI, and logistics, working together to push the boundaries of automation.

We’re looking for talented and ambitious individuals to join us on this journey—helping shape the future of robotics while growing alongside a world-class team. If you're passionate about innovation, problem-solving, and making a real-world impact, we want to hear from you!

Contoro Robotics is an Austin-based company building autonomous truck-unloading robots for warehouse operations. We deploy reliable, high-throughput robotic systems that handle heavy, unstructured freight in real logistics settings every day.

We are looking for a Robotics Engineer to help improve the reliability and performance of our robot's motion-planning stack on a live production fleet. A significant part of this role is hands-on production support: reproducing field issues, debugging them across motion planning, perception, and controls, and validating fixes on real hardware. You will work closely with our senior motion-planning and autonomy engineers, tuning and integrating motion planning as you grow your depth in the field.

Production Debugging and Performance Support

• Investigate and help resolve production issues that affect cycle time, pick success, and operator intervention rate, working across motion planning, perception, and controls to find root cause.

• Reproduce field failures on real hardware, analyze logs and telemetry, and work with senior engineers to validate root cause and ship fixes.

Motion Planning Implementation and Integration

• Implement and tune planners within the MoveIt ecosystem for collision-free, kinematically feasible motion, with guidance from senior engineers.

• Integrate perception outputs (container frame, box poses, occupancy) into the planning scene, reasoning about collision objects such as container walls, ceiling, and neighboring boxes.

• Integrate path and trajectory generation with the control stack (MoveIt and ros_control), and measure cycle-time and throughput impact on hardware.

Hardware Validation

• Run controlled tests on the robot, compare before-and-after throughput, and quantify the impact of changes across diverse box and container configurations.

• Collaborate across autonomy, perception, controls, and robot software to deliver reliable end-to-end motion.

Experience: 1-3 years of professional experience (or equivalent research or internship experience) in motion planning, manipulator control, or robotics software, ideally with real-hardware exposure. Strong recent graduates with excellent fundamentals are encouraged to apply.

Education: Minimum B.S. in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field.

Technical Skills:

• Proficient in C++ (modern standards), Python, and ROS 1 or ROS 2.

• Working knowledge of MoveIt and at least one motion planning approach (e.g., sampling-based planners such as RRT/RRT-Connect/PRM, or optimization-based planners such as CHOMP/TrajOpt).

• Solid grasp of manipulator kinematics - forward/inverse kinematics, collision checking, velocity and acceleration limits, and singularity and joint-limit awareness for 6/7-DOF arms.

• Strong debugging instincts and a willingness to dig into hard, ambiguous production issues.

• Good communication and eagerness to learn from senior engineers.

• Hands-on experience with industrial manipulators (e.g., UR, KUKA) and real-time joint control.

• Familiarity with integrating perception inputs (point clouds, object poses, occupancy maps) into collision-aware planning.

• Exposure to time-parameterization tools (TOTG/TOPP-RA, Ruckig, jerk-limited/S-curve profiles).

• A controls or electrical background (motor limits, torque, current) useful for hardware-level debugging.

• Exposure to physics-based or kinematic simulation (Isaac Sim, Gazebo, MuJoCo, Bullet).