Senior Motion Planning Engineer - Safety-Layer

Motional • Boston, MA

7d • $172,000 - $229,000

About The Position

The Senior Motion Planning Engineer will help build a robust safety layer for Motional’s autonomous vehicles. The Safety-Layer Team refines upstream motion plans to meet safety-driven requirements, plans dynamic evasive maneuvers, and implements system-wide safety overrides. The work involves algorithm design and documentation, software development, metrics definition, simulation implementation and evaluation, and in-vehicle testing. The role also maintains cross-functional relationships to understand and communicate how safety-layer work affects overall autonomous-vehicle performance.

The engineer will design, implement, and test trajectory-synthesis and trajectory-selection algorithms across asynchronous generation sources, develop evasive planning in coordination with low-level control systems, and evaluate how perception sensor modalities can improve identification of safety-critical scenarios. The role includes building test and analysis tools, improving and validating dynamic simulations against real-world data, defining performance, comfort, and safety metrics, and analyzing fleet data. The engineer will also support root-cause analysis of simulation, closed-course, and public-road events from a motion-planning and safety perspective. In-person vehicle testing at Motional and partner deployment and test-track locations is typically required once every four to six months.

Requirements

• At least 3 years of industrial experience developing motion-planning and collision-avoidance algorithms and components; autonomous-vehicle industry experience is preferred.
• Strong background in modern C++ programming, including C++17 or C++20, with emphasis on high-performance and reliable systems.
• Experience with motion-planning concepts, including trajectory generation through numerical optimization and sampling-based approaches; cost- and heuristic-based trajectory selection; multimodal 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.

Nice To Haves

• Master’s or Ph.D. in Control Theory, Robotics, Computer Science, Applied Mathematics, or a related field.
• General familiarity with machine learning, including supervised learning, reinforcement learning, and training/test validation.
• Familiarity with vehicle-dynamics modeling, including standard longitudinal and lateral vehicle-dynamics models.
• Publications in robotics, controls, or autonomous-driving conferences or journals, including IROS, ICRA, CoRL, RAL, TRO, CDC, ACC, TAC, CSL, Automatica, IV, ITSC, or T-ITS.
• Familiarity with SQL-based queries.
• Familiarity with Python.
• Familiarity with GitLab, Bazel, VSCode, or CLion.
• Familiarity with autonomous-vehicle industry simulation tooling.

Responsibilities

• Design, implement, and test algorithms that synthesize and select trajectories across asynchronous generation sources while ensuring adherence to safety-critical requirements.
• Develop evasive-maneuver planning algorithms in conjunction with low-level control systems to use the vehicle’s actuation capacity safely for collision avoidance.
• Evaluate the role of multiple perception sensor modalities in identifying 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.
• Use, validate, and improve dynamic simulation environments, including validating simulation models against real-world data.
• Define and implement performance, comfort, and safety metrics for motion-planning and safety systems.
• Analyze fleet-wide vehicle data quantitatively to validate design improvements and identify areas for improvement.
• 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 and test-track locations, typically once every four to six months.
• Maintain cross-functional relationships and communicate the effects of the team’s work on overall autonomous-vehicle performance.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k) with company match
• Health savings accounts
• Life insurance
• Pet insurance
• The role may include a bonus or company equity; eligibility and details are not specified.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed