Staff, Software Engineer - Motion Planning (Unstructured Environments)

Blacksburg, VA · Remote · full time · Staff

$186,200 – $223,400

Listed on Torc Robotics’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

This Staff-level role leads motion planning and trajectory optimization systems for autonomous trucks, requiring deep expertise in robotics algorithms, C++ development, and real-time system design. It suits experienced engineers who have led cross-functional technical initiatives and want to shape autonomous vehicle architecture at scale.

Our summary, not Torc Robotics’s wording. The full posting is on their site.

Skills this role names

What they ask for

Required

Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related field plus 10+ years industry experience, or Master's degree plus 7+ years, or PhD plus 5+ years
Proven Staff or Principal-level technical leadership across multiple engineering teams
Deep expertise in robotics, autonomous vehicle, ADAS, or complex real-time software systems
Deep expertise in modern C++ within Linux environments
Motion planning, trajectory generation, trajectory optimization, behavior planning, or decision-making experience for robotics or autonomous systems
Strong understanding of vehicle dynamics, kinematics, and planning architecture
Foundation in linear algebra, numerical optimization, geometry, and robotics algorithms
Hands-on experience analyzing and optimizing metrics in resource-constrained environments
Troubleshooting skills for real-time diagnostics and post-analysis
Mastery of software engineering fundamentals and scalable development practices
Version control with Git, build systems, and package management
Test automation principles and proficiency with unit testing frameworks
Strong problem-solving and complex system-level debugging
Excellent communication and collaboration within cross-functional teams
U.S. citizenship

Nice to have

Model Predictive Path Integral (MPPI)
Model Predictive Control (MPC)
Nonlinear optimization
Machine learning techniques such as imitation learning or reinforcement learning
Hybrid planning architectures
Machine learning model integration into production systems
Experience with simulation platforms and large-scale autonomy validation
Robotics frameworks such as ROS, ROS2, Autoware, or Apollo
CUDA, GPU acceleration, TensorRT, or high-performance compute
On-vehicle testing, debugging, and deployment support
Open-source contributions related to embedded Linux, robotics, or deployment automation