Staff, Software Engineer - Motion Planning (Unstructured Environments)

Torc Robotics is a leader in autonomous driving technology focused on developing software for automated trucks. They are seeking a Staff Motion Planning Engineer to lead technical development for planning, prediction, and decision-making systems in unstructured environments, ensuring safe and efficient navigation for autonomous vehicles.

Responsibilities:

• Defining and maintaining the architectural vision across teams, platforms, and services to ensure scalability, reliability, maintainability, long-term technical alignment, and consistency of software design patterns
• Supporting Engineering Managers and Senior ICs in resolving complex technical design, architecture, and implementation challenges across multiple domains while balancing feasibility against business objectives
• Establishing and promoting engineering best practices for code quality, automated testing, performance optimization, monitoring, security, and operational excellence
• Conducting architecture reviews, technical deep dives, design sessions, and technical assessments to guide engineering decisions and solution direction
• Mentoring and coaching senior and mid-level engineers while helping grow future Staff and Principal engineers through technical guidance and leadership development
• Fostering a culture of engineering excellence, documentation, collaboration, innovation, and knowledge sharing across the organization
• Translating complex technical concepts, trade-offs, and architectural decisions into clear recommendations for leadership and cross-functional stakeholders
• Leading major technical initiatives such as platform modernization, observability enhancements, performance optimization, scalability improvements, and reliability engineering efforts
• 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
• Collaborate closely with Safety, Controls, Perception, Validation, and Simulation teams to develop safe and reliable autonomous driving behaviors
• Develop and execute validation strategies across Software-in-the-Loop (SiL), Hardware-in-the-Loop (HiL), and Vehicle-in-the-Loop (ViL) environments
• 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
• Perform on-vehicle troubleshooting using real-time diagnostic tools and in-depth log analysis

Requirements:

• Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field with: 10+ years of industry experience OR Master's degree with 7+ years of experience OR PhD with 5+ years of experience
• Proven experience operating as a Staff, Principal, or multi-team technical leader within complex engineering organizations including experience leading large cross-functional technical initiatives across multiple engineering teams
• Deep expertise developing robotics, autonomous vehicle, ADAS, or other complex real-time software systems
• Deep expertise in modern C++ development within Linux-based environments
• Diverse experience developing motion planning, trajectory generation, trajectory optimization, behavior planning, or decision-making systems for robotics or autonomous systems
• Strong understanding of vehicle dynamics, vehicle kinematics, trajectory feasibility, and planning system architecture
• Strong foundation in linear algebra, numerical optimization, geometry, and robotics algorithms
• Hands-on experience with analyzing metrics to optimize performance in a resource-constrained environment
• Excellent troubleshooting skills with the ability to perform both real-time diagnostics and in-depth post-analysis
• Mastery of software engineering fundamentals, system design principles, and scalable development practices
• Experience with version control (Git), build systems, and package management
• Understanding of test automation principles and proficiency with unit testing and test automation frameworks (pytest, Robot Framework, gtest, etc.)
• 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 Model Predictive Path Integral (MPPI), Model Predictive Control (MPC), convex optimization, quadratic programming (QP), nonlinear optimization, or other optimization-based planning techniques for autonomous vehicles
• Experience applying machine learning techniques such as imitation learning, reinforcement learning, or hybrid planning architectures
• 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 supporting on-vehicle testing, debugging, and deployment activities
• Passion for autonomous vehicles, robotics, and solving complex real-world engineering challenges
• Contributions to open-source projects related to embedded Linux, robotics, or deployment automation