Senior Motion Planning Engineer - Safety-Layer
Motional · Boston, Massachusetts, United States; Las Vegas, Nevada, United States; Pittsburgh, Pennsylvania, United States; Remote U.S.
About this role
**Mission Summary** The Safety-Layer Team ensures Motional’s Autonomous Vehicle executes appropriate dynamic trajectories in both nominal and safety-critical scenarios. You’ll help refine upstream motion plans to meet safety-driven requirements, enable highly dynamic evasive maneuvers at the vehicle’s physical limits, and implement system-wide safety overrides—through rigorous algorithm design, implementation, validation, and evaluation in simulation and on closed-course/public roads. **What you will work on** - Design, implement, and test algorithms for synthesizing and selecting trajectories across asynchronous generation sources to ensure safety-critical requirements are met. - Develop evasive maneuver planning algorithms in collaboration with low-level control systems to safely use the AV’s full actuation capacity for collision avoidance. - Evaluate how multiple perception sensor modalities improve the safety-layer’s identification of safety-critical scenarios. - Build test and analysis tooling for isolated and system-level evaluation of safety-layer components and processes; select test parameters and analyze results. - Utilize, validate, and improve dynamic simulation environments to accelerate safety-critical motion planning algorithm design and analysis (including validating simulation models against real-world data). - Define and implement performance, comfort, and safety metrics for motion planning and safety systems; perform quantitative analysis of fleet-wide vehicle data. - Support root-cause analysis of simulation, closed-course, and public road events from a motion planning and safety perspective. - Travel for in-person vehicle testing at Motional and partner deployment/test track locations, typically once every 4–6 months. **Desired Background** - MS or PhD preferred in Control Theory / Robotics / Computer Science / Applied Math (or related). - 3+ years developing motion planning and collision avoidance algorithms/components in an industrial setting (preferably AV). - Strong modern C++ experience (C++17/C++20) with a focus on high-performance, reliable systems. - Familiarity with motion planning concepts including numerical optimization and sampling-based trajectory generation; cost/heuristic-based trajectory selection; multi-modal prediction; geometric and physics-based collision detection; time-series processing; and data visualization. - Familiarity with dynamic collision modeling, convex set processing, state machine logic, and multi-rate decision-making. - Familiarity with agentic-based development tooling. - Familiarity with Linux-based operating systems. **Bonus Points** - Familiarity with machine learning (supervised learning, reinforcement learning, training/validation). - Vehicle dynamics modeling experience (longitudinal/lateral dynamics models). - Publications in Robotics/Controls/Autonomous Driving (e.g., IROS, ICRA, CoRL, RAL, TRO, CDC, ACC, TAC, CSL, Automatica, IV, ITSC, T-ITS). - Familiarity with SQL. - Familiarity with Python. - Familiarity with GitLab, Bazel, VSCode / CLion. - Familiarity with AV industry simulation tooling. **Compensation & Benefits (Summary)** - Estimated salary range (base salary only): **$172,000–$229,000 USD** - Additional compensation may include bonus and/or company equity. - Benefits may include medical, dental, vision, 401(k) with match, health savings accounts, life insurance, pet insurance, and more. **About Motional** Motional is a driverless technology company making
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