Sr. Robotics Engineer, Motion Planning

Contoro Inc.•Austin, TX

5d•Onsite

About The Position

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 Senior Robotics Engineer to lead the next phase of motion-performance improvement for our robot arm. You will be the technical lead for motion planning and trajectory generation, owning how we turn planned motion into fast, smooth, dynamically feasible trajectories under real hardware constraints. Your focus is reducing cycle time while maintaining safety and payload stability, using strong fundamentals in kinematics, dynamics, constrained optimization, and hardware-backed performance validation. This is a senior individual-contributor role: you set technical direction for motion planning, own the hardest trajectory-optimization problems, and mentor other engineers, working closely with the autonomy team.

Requirements

5+ years of professional experience in motion planning, trajectory optimization, or manipulator control, with production or real-hardware deployment and a track record of owning motion performance end-to-end.
B.S. or M.S. in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field (or equivalent industry experience).
Strong grounding in classical trajectory generation and time-parameterization methods (e.g., TOTG/TOPP-RA, Ruckig, S-curve profiles), with the ability to derive, adapt, and implement them in production systems.
Deep grasp of manipulator kinematics and dynamics - forward/inverse kinematics, collision checking, velocity/acceleration/jerk and torque constraints, and singularity and joint-limit handling for 6/7-DOF arms.
Background in optimization-based motion (optimal control, QP/NLP-based trajectory optimization).
Deep proficiency in C++ (modern standards), Python, and ROS 1 or ROS 2.
Experience validating cycle-time and reliability improvements on real hardware, backed by simulation.
Strong problem-solving and data-driven decision-making; able to own ambiguous, high-impact problems and decompose them into actionable work.
Excellent communication - able to present complex technical concepts clearly to cross-functional teams and mentor other engineers.

Nice To Haves

Hands-on expertise with MoveIt and motion planning frameworks (OMPL, RRT/RRT-Connect/PRM, CHOMP/TrajOpt).
Experience with industrial manipulators (e.g., KUKA) and real-time joint control.
Experience planning for multi-object or multi-pick manipulation.
Experience optimizing for throughput or cycle time in a production robotics or logistics setting.
Experience with GPU-accelerated or learning-based motion planning in addition to classical methods.
Experience with physics-based or kinematic simulation for planning validation (Isaac Sim, Gazebo, MuJoCo, Bullet).

Responsibilities

Own trajectory generation and time parameterization: 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.
Drive the next generation of our motion performance through advanced time parameterization, optimization-based motion (optimal control, QP/NLP), and payload-aware planning that meaningfully increases throughput.
Ensure smooth, jerk-limited motion that maintains payload stability and hardware safety, and robustly handle near-singularity and joint-limit edge cases without stalls or unsafe motion.
Own geometric path planning for a high-DOF manipulator moving boxes through cluttered, partially occluded container environments.
Evaluate and evolve the planning architecture and toolchain (e.g., MoveIt, OMPL, CHOMP/TrajOpt), making the build, adopt, and extend decisions for the planning stack.
Integrate perception outputs (container frame, box poses, occupancy) into the planning scene, reasoning about collision objects such as container walls, ceiling, and neighboring boxes.
Define motion-performance metrics, benchmark alternative approaches, and validate gains both in simulation and on real hardware.
Use physics-based and kinematic simulation to develop and de-risk changes before they reach the fleet.
Set technical direction for motion planning and drive multi-quarter improvements in cycle time and reliability.
Mentor junior and mid-level engineers and raise the team's bar on motion-planning craft, testing, and reliability.
Collaborate across autonomy, perception, controls, and robot software to deliver end-to-end motion that is both fast and reliable across diverse box configurations.