Senior Motion Planning Engineer - Safety-Layer

Motional•Boston, MA

7d•$172,000 - $229,000

About The Position

The Safety-Layer Team ensures Motional autonomous vehicles execute appropriate dynamic trajectories in nominal and safety-critical scenarios. It refines upstream motion plans to meet safety requirements, plans dynamic evasive maneuvers at the vehicle’s physical limits, and implements system-wide safety overrides through algorithm design, implementation, validation, simulation analysis, and closed-course and public-road evaluation. The Senior Motion Planning Engineer will help build a robust motion-planning safety layer and enable capabilities for Motional’s autonomous vehicles. Work includes algorithm design and documentation, software development, metrics definition, simulation implementation and evaluation, and in-vehicle testing. The role also involves maintaining cross-functional relationships to understand and communicate how the team’s work affects overall autonomous-vehicle performance. Travel for 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 modern C++ programming background (C++17 or C++20), emphasizing high-performance and reliable systems.
Experience with motion-planning concepts, including trajectory generation using 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, and VSCode or CLion.
Familiarity with autonomous-vehicle industry simulation tooling.

Responsibilities

Design, implement, and test algorithms for synthesizing and selecting trajectories from asynchronous generation sources to ensure vehicle behavior complies with 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 how multiple perception-sensor modalities can improve the safety layer’s identification of safety-critical scenarios.
Build test and analysis tools 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 for 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; analyze fleet-wide vehicle data quantitatively to validate 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 to understand and communicate the impact of the team’s work on overall autonomous-vehicle performance.