Senior Motion Planning Engineer - Safety-Layer

Motional is a driverless technology company developing autonomous vehicles. This role focuses on the Safety-Layer Team, responsible for ensuring autonomous vehicles execute appropriate dynamic trajectories in both nominal and safety-critical scenarios. The Senior Motion Planning Engineer will design algorithms, develop evasive maneuver planning capabilities, and validate safety-critical systems through simulation and real-world testing.

• Design, implement, and test algorithms for synthesizing and selecting trajectories to ensure AV behaviors comply with safety-critical requirements
• Develop evasive maneuver planning algorithms in conjunction with low-level control systems to safely use full actuation capacity for collision avoidance
• Evaluate multiple perception sensor modalities in identifying and responding to safety-critical scenarios
• Construct test and analysis tooling for isolated and system-level evaluation of safety-layer components; select test parameters and analyze results
• Define and implement performance, comfort, and safety metrics; perform quantitative analysis of fleet-wide vehicle data to validate improvements
• Travel for in-person vehicle testing at deployment and test track locations, typically once every 4 to 6 months

• Master's or PhD degree in Control Theory, Robotics, Computer Science, Applied Mathematics, or related field
• 3+ years of experience developing motion planning and collision avoidance algorithms and components in an industrial setting, preferably autonomous vehicles
• Strong C++ programming skills (C++17, C++20) with emphasis on high-performance and reliable systems
• Experience with motion planning concepts including trajectory generation via numerical optimization and sampling-based approaches, cost/heuristic-based trajectory selection, multi-modal prediction, collision detection, and time-series data processing
• Familiarity with Linux-based operating systems
• Knowledge of dynamic collision modeling, convex set processing, state machine logic, and multi-rate decision-making

• General familiarity with machine learning including supervised learning, reinforcement learning, and training/test validation
• Familiarity with vehicle dynamics modeling and standard longitudinal/lateral vehicle dynamics models
• Publications in robotics, controls, or autonomous driving conferences/journals (IROS, ICRA, CoRL, RAL, TRO, CDC, ACC, TAC, CSL, Automatica, IV, ITSC, T-ITS)
• Experience with SQL-based queries, Python, Git Lab, Bazel, VSCode or CLion, and AV industry simulation tooling

Base salary $172,000–$229,000 USD. This role may include additional forms of compensation such as bonus or company equity. Benefits include medical, dental, vision, 401k with company match, health savings accounts, life insurance, and pet insurance.