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- Motion Planning Algorithm Tech Lead
JOB ID 28252
Motion Planning Algorithm Tech Lead
- Manufacturer, Manufacturer-Automotive
- IT, Product Engineer (Hard/soft)
- Tokyo
- ¥15,000,000～¥25,000,000
A Japanese deep tech startup founded in April 2024. Specializing in the development of autonomous driving systems for large trucks, it aims to solve challenges faced by Japan's logistics industry, such as driver shortages, aging populations, and increasing logistics demand. Focusing on trunk line transportation using expressways, it promotes improved transport safety, operational efficiency, and reduced logistics costs through advanced AI-powered autonomous driving technologies. Through collaboration with automakers and logistics companies, it is working on the practical use of autonomous trucks and the construction of next-generation logistics infrastructure.
A Japanese deep-tech startup established in April 2024, specializing in the development of autonomous driving systems for heavy-duty trucks. The company aims to transform Japan’s logistics industry by addressing challenges such as driver shortages, an aging workforce, and increasing freight transportation demand. Focusing on autonomous highway transportation, it develops advanced AI-powered technologies designed to improve transportation safety, operational efficiency, and logistics sustainability. Through collaboration with automotive manufacturers and logistics companies, the company is working toward the commercialization of autonomous trucking solutions and the development of next-generation automated freight transportation infrastructure in Japan.
This role leads the development of optimization-based planning algorithms for autonomous trucks. The position owns the design, implementation, and continuous improvement of the planning module, with close integration across perception, prediction, control, simulation, and on-vehicle testing.
Job Description
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- Responsibilities
- ・Lead the design, implementation, and optimization of decision-making, path planning, trajectory generation, and speed planning algorithms for autonomous trucking scenarios.
・Build planning solutions based on model predictive control, convex optimization, numerical optimization, search-based planning, and related algorithmic methods.
・Define the architecture, interfaces, data contracts, and performance requirements of the planning module from a full-system perspective.
・Collaborate with perception, prediction, and control teams to improve planning robustness under perception uncertainty, dynamic traffic interaction, and vehicle dynamics constraints.
・Drive algorithm iteration through simulation, log replay, closed-course testing, and road-test feedback, including metric design, issue diagnosis, and performance regression analysis.
・Lead a team of planning engineers, including technical direction setting, design review, code review, task decomposition, and engineering quality control.
・Translate research or prototype-level methods into production-quality C++ software that can be tested, debugged, integrated, and maintained in a real-time autonomous driving stack.
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- Requirements
- ・Strong experience in autonomous driving planning, including motion planning, path planning, behavior planning, trajectory optimization, or speed planning.
・Deep understanding of MPC, convex optimization, and numerical optimization, with hands-on experience applying optimization methods to real autonomous driving or robotics systems.
・Strong C++ programming capability, with the ability to build reliable, maintainable, and performance-aware algorithm software.
・Experience defining module boundaries and interfaces across perception, prediction, planning, and control in a complex robotics or autonomous driving system.
・Proven experience as a Tech Lead or engineering manager, including leading a team, reviewing technical designs, mentoring engineers, and driving delivery quality.
・Solid mathematical foundation in optimization, control, probability, linear algebra, or related areas.
・Ability to debug planning behavior using simulation results, logs, vehicle data, and system-level signals rather than relying only on offline algorithm metrics.
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- Preferred
- ・Experience with autonomous trucks, commercial vehicles, or high-speed highway autonomous driving scenarios.
・Experience with ROS or ROS2 development in robotics or autonomous driving systems.
・Familiarity with search-based planning methods such as Hybrid A*, State Lattice, MCTS, or graph-search variants.
・Familiarity with decision-making under uncertainty, including MDP, POMDP, reinforcement learning, inverse reinforcement learning, or probabilistic graphical models.
・Experience with modern control methods, including nonlinear control, optimal control, and vehicle dynamics modeling.
・CUDA or GPU acceleration experience for planning, optimization, simulation, or performance-critical workloads.
・Experience building planning evaluation metrics, scenario libraries, simulation workflows, or road-test feedback loops.
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- Location
- Tokyo
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- Work Style
- Remote working possible, Flex working possible
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- Salary
- ¥15,000,000～¥25,000,000
-
- Attractive
Points - Japanese company with global opportunities, Weekends and holidays off, Major corporation
- Attractive
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