Senior Motion Planning Engineer - Trajectory Optimization Remote - U.S, Ann Arbor, MI, Fort Wor

About the Company

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.

A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.

Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.

Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

Join Torc's Behaviors Team, where we develop the planning, prediction, and decision-making systems that determine how our autonomous trucks navigate the world safely, efficiently, and predictably. Our team operates at the intersection of robotics, machine learning, optimization, simulation, and safety, building the core autonomy capabilities that translate perception into action.

• Design, develop, and optimize motion planning algorithms including trajectory generation, trajectory selection, behavior planning, and optimization-based planning approaches for autonomous trucks.
• Develop planning solutions leveraging techniques such as graph search, sampling-based planning, optimization-based planning, spline/B-spline trajectories, convex optimization, and Frenet-frame approaches.
• Incorporate vehicle kinematic and dynamic constraints into planning systems to ensure safe, feasible, and comfortable vehicle behavior.
• Develop production-quality software using modern C++ within a Linux environment while adhering to quality, safety, testing, and deployment best practices.
• Participate in software architecture discussions and contribute to technical designs that support scalable and maintainable autonomy systems.
• Develop and execute validation strategies across Software-in-the-Loop (SiL), Hardware-in-the-Loop (HiL), and Vehicle-in-the-Loop (ViL) environments.
• Collaborate closely with Safety, Controls, Perception, Validation, and Simulation teams to develop safe and reliable autonomous driving behaviors.
• Investigate and debug vehicle behavior by reproducing issues in simulation, analyzing system performance, and implementing software improvements.
• Support vehicle integration, deployment activities, and post-deployment investigations to ensure reliable autonomy performance.
• Participate in technical design reviews, code reviews, and continuous improvement initiatives across the Behaviors organization.
• Mentor junior engineers through collaboration, technical guidance, and knowledge sharing.

• Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field with 5+ years of industry experience; OR Master's degree with 3+ years of experience; OR PhD with 1+ years of experience.
• Strong proficiency in modern C++ development within Linux-based environments.
• Experience developing robotics, autonomous vehicle, ADAS, or other complex real-time software systems.
• Strong experience developing motion planning, trajectory generation, trajectory optimization, behavior planning, or decision-making systems for robotics or autonomous systems.
• Solid understanding of vehicle dynamics, vehicle kinematics, trajectory feasibility, and planning system architecture.
• Strong foundation in linear algebra, numerical optimization, geometry, and robotics algorithms.
• Strong understanding of software engineering fundamentals, system design principles, and scalable development practices.
• Experience working across the full software development lifecycle, from design and implementation through validation, deployment, and operational support.
• Strong problem-solving skills and the ability to debug complex system-level issues.
• Excellent communication and collaboration skills within cross-functional engineering teams.
• Ability to work independently while contributing effectively within a highly collaborative environment.

• Experience with motion planning, trajectory optimization, behavior prediction, or decision-making systems for autonomous vehicles.
• Experience with Model Predictive Control (MPC), convex optimization, quadratic programming (QP), nonlinear optimization, or other optimization-based planning techniques.
• Experience applying machine learning techniques such as imitation learning, reinforcement learning, or hybrid planning architectures.
• Understanding of vehicle dynamics, controls, state estimation, and trajectory tracking.
• Experience developing software in both C++ and Python environments.
• Experience integrating machine learning models into real-time or production systems.
• Experience working with simulation platforms and large-scale autonomy validation environments.
• Experience with robotics frameworks such as ROS/ROS2, Autoware, Apollo, or similar autonomy platforms.
• Exposure to CUDA, GPU acceleration, TensorRT, or high-performance compute environments.
• Experience…