Senior Motion Planning Engineer - Safety-Layer

Mission Summary:The Safety-Layer Team is responsible for ensuring the Motional Autonomous Vehicle executes appropriate dynamic trajectories in both nominal and safety-critical scenarios.

Mission Summary: The Safety-Layer Team is responsible for ensuring the Motional Autonomous Vehicle executes appropriate dynamic trajectories in both nominal and safety-critical scenarios.

This team focuses on refining upstream motion plans to guarantee compliance with safety-driven requirements, executing highly dynamic evasive maneuvers at the AV’s physical limits, and implementing system-wide safety overrides.

The team pursues these goals through a process of rigorous algorithmic design, implementation, validation, analysis in simulation, and comprehensive closed-course and public road evaluation.

As the team's responsibilities are growing to support more complex scenarios and the scaling of Motional ’s deployment area, we are looking for a Senior Motion Planning - Safety-Layer to help build a robust motion planning safety-layer.

You will work on enabling cutting-edge capabilities for Motional ’s AV, including algorithm design and documentation, software development, metrics definition, simulation implementation and evaluation, and in-vehicle testing.

As part of this role, you will maintain cross-functional relationships across the organization to help understand and communicate the impacts of their work on overall AV performance.

What you will work on: Design, implement, and test algorithms for synthesizing trajectories and selecting trajectories across asynchronous generation sources to ensure AV behaviors adhere to safety-critical requirement specifications.

Develop evasive maneuver planning algorithms in conjunction with low-level control systems to safely use the full actuation capacity of Motional ’s AV for collision avoidance. Evaluate the role of multiple perception sensor modalities in refining the safety-layer’s identification of safety-critical scenarios.

Construct test/analysis tooling for isolated and system-level evaluation of safety-layer components and processes, select test parameters, and analyze test results.

Utilize, validate, and improve dynamic simulation environments to accelerate the design and analysis of safety-critical motion planning algorithms, including validating simulation models against real-world data.

Define and implement performance, comfort, and safety metrics for motion planning and safety systems, and perform quantitative analysis of fleet-wide vehicle data to validate design improvements and identify key areas of improvement.

Build a system-wide understanding of Motional ’s autonomy stack components by supporting 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 ’s and our partner's deployment and test track locations, typically once every 4 to 6 months.

Desired Background: MS, PhD preferred, in one of Control Theory / Robotics / Computer Science / Applied Math or a related field 3+ years of experience developing motion planning and collision avoidance related algorithms and components in an industrial setting, preferably the AV industry Strong background in modern C++ programming (C++17, C++20), with an emphasis on developing high-performance and reliable systems Experienced with concepts from motion planning including trajectory generation through numerical optimization and sampling-based approaches, cost and heuristic-based methods for trajectory selection, multi-modal prediction models, geometric and physics-based collision detection, time-series data processing, and data visualization Familiarity with dynamic collision modeling, convex set processing, state machine logic, and multi-rate decision-making processes Familiarity with agentic-based development tooling Familiarity with Linux-based operating systems Bonus Points: General familiarity with machine learning, including supervised learning, reinforcement learning, training/test validation, etc.

Familiarity with vehicle dynamics modeling, including standard longitudinal/lateral vehicle dynamics models Publicati