Senior Motion Planning Engineer
Woven by Toyota
- Palo Alto, CA Ann Arbor, MI
- $140,000-230,000 per year
- Permanent
- Full-time
- Develop state-of-the-art algorithms and software for the motion planning module to deliver desired driving behaviors in various driving scenarios.
- Build metrics to measure, validate, and improve performance through testing in simulation and on roads.
- Collaborate closely with teams such as Perception, Simulation, Infrastructure, Integration, Tooling to drive unified solutions.
- Incorporate data-driven approaches to improve system performance and unlock new capabilities for safety or peace of mind.
- Design reusable software components as part of an integrated system.
- Understand and fulfill the software practices that produce maintainable code, including simulation, continuous integration, code review, HIL testing, and in-vehicle testing.
- You possess an M.S., Ph.D., or equivalent, in Robotics, Control, Computer Science, Applied Mathematics, or other quantitative fields.
- You have 3+ years of experience in development of motion planning algorithms for autonomous vehicles. Additionally, your expertise ideally includes the implementation of efficient computational geometry and linear algebra methods.
- Experience with decision making, motion planning techniques like trajectory optimization, sampling-based planning, model predictive control, and Machine Learning for Motion Planning.
- You are proficient in C++, and may have familiarity with Python or other programming languages.
- You are an excellent communicator, skilled collaborator, and principled colleague.
- You have strong R&D potential in algorithm design, data-driven approaches to safety, and large-scale systems architecture.
- You have a strong, practical understanding of real-time system development, performance issues, testing modalities, and tradeoffs.
- Your experience with code compliance and embedded systems is highly regarded.
- Your familiarity with hardware-in-the-loop design is recommended.
- (Nice to Have) Hands-on experience with building a planning stack for autonomous robots.