Source-listed Job

Senior Motion Planning Engineer - Safety-Layer

Mission Summary:

Job Remote Source description available
Motional Boston, Boston, Las Vegas, Pittsburgh, Remote U.S. Only Source published Oct 16, 2026 Source retrieved Oct 16, 2026
Source: Motional (greenhouse) · A retrieval date records when our system last obtained the source record. It does not guarantee the vacancy is still open or that every detail has been independently checked.
Description from the source The source description is formatted below for discovery. The provider owns the original wording and may change its requirements or close applications.
Work modeRemote / location-flexible

Overview

Mission Summary:

Full job description

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 Publications in Robotics/Controls or Autonomous Driving conferences/journals (IROS, ICRA, CoRL, RAL, TRO, CDC, ACC, TAC, CSL, Automatica, IV, ITSC, T-ITS) Familiarity with SQL-based queries Familiarity with Python Familiarity with Gitlab, Bazel, VSCode / CLion Familiarity with AV industry simulation tooling #LI-BP1 The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. Salary Range $172,000 — $229,000 USD Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. Our journey is always people first. We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move. Higher purpose, greater impact. We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.   Scale up, not starting up. Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges. Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit  www.Motional.com and follow us on Twitter , LinkedIn ,  Instagram and YouTube . Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.

Tips for this job

Practical JobOpportunity guidance. These tips do not replace official rules or create new eligibility requirements.

  1. Tailor the CV and application to the responsibilities and required skills stated on the official employer page.
  2. Use concrete evidence of relevant work, projects and measurable results rather than generic claims.
  3. Confirm location, work authorization, remote restrictions and sponsorship terms before applying.
  4. Apply through the original employer or official recruitment destination shown on this page.
Original authoritative source

JobOpportunity.info helps you discover and organize source listings. Confirm eligibility, dates, salary/funding and application instructions on the original source before submitting anything.

Apply through JobOpportunity →

Browse current JobOpportunity listings from Motional (greenhouse) →

More ways to save

Discover deals, coupons and free courses on our sister site.

Explore DealVorio
Save more with DealVorio: deals, coupons, free courses, apps and books