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!

Title: Robotics Engineer, Motion Planning

Contoro Robotics is an Austin-based startup revolutionizing warehouse automation with a cutting-edge autonomous truck unloading solution capable of handling payloads over 60 lbs. Our mission is to deploy reliable, high-throughput robotic systems that solve real logistics challenges daily—beyond proof-of-concept demos. We’re seeking a Robotics Engineer specializing in motion planning to own both halves of how our robot moves: the geometric path the arm and gripper take to transport boxes from the trailer to the dropoff zone, and the time-optimal, jerk-limited trajectory that makes that motion fast and reliable. You’ll work closely with our Autonomy lead to maximize throughput inside tightly constrained container environments.

Path Generation

• Design and implement geometric path planning for a high-DOF manipulator transporting single and multiple boxes through cluttered, partially-occluded container spaces.

• Develop and tune sampling-based and optimization-based planners (e.g., OMPL/RRT-family, CHOMP/TrajOpt) within the MoveIt ecosystem for collision-free, kinematically-feasible motion.

Trajectory Optimization

• Turn planned paths into time-optimal, dynamically-feasible trajectories that minimize cycle time while respecting velocity, acceleration, jerk, and torque limits of the arm and payload.

• Ensure smooth, jerk-limited motion that protects payload stability (no dropped or shifted boxes) and hardware longevity; handle near-singularity and joint-limit edge cases without stalls or unsafe motion.

Collision & Environment Awareness

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

Integration & Validation

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

• Collaborate with the Autonomy lead, Orchestration, Perception, and Controls to deliver end-to-end motion that is both fast and reliable across diverse box configurations.

Experience: 3+ years of professional experience in motion planning, trajectory optimization, or manipulator control, with production or real-hardware deployment.

Education: Minimum B.S. in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field (or equivalent industry experience).

Technical Expertise:

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

• Hands-on with MoveIt and motion planning frameworks (OMPL, sampling-based planners such as RRT/RRT-Connect/PRM, and/or optimization-based planners such as CHOMP/TrajOpt).

• Hands-on with time-optimal trajectory generation and time parameterization (e.g., TOTG/TOPP-RA, Ruckig, jerk-limited/S-curve profiles).

• Strong grasp of manipulator kinematics and dynamics—forward/inverse kinematics, collision checking, velocity/acceleration/jerk and torque constraints, singularity and joint-limit handling for 6/7-DOF arms.

• Experience integrating perception inputs (point clouds, object poses, occupancy maps) into collision-aware planning, and validating cycle-time improvements on hardware.

Soft Skills:

• Strong problem-solving skills and data-driven decision-making.

• Excellent communication—capable of presenting complex technical concepts clearly to cross-functional teams.

• Experience with industrial manipulators (e.g., KUKA) and real-time joint control.

• Background in optimization-based motion (optimal control, QP/NLP-based trajectory optimization).

• Experience planning for multi-object or multi-pick manipulation.

• Experience optimizing for throughput/cycle-time in a production robotics or logistics setting.

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