Robotics Engineer, Motion Planning

Join Contoro Robotics – Revolutionizing Warehouse Automation with Cutting-Edge Robotics

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!

Job Description

Title: Robotics Engineer, Motion Planning

Intro

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.

Job Responsibility

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.
  
  

Qualification/Requirements

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.
  
  

Preferred/Plus

• 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).