Applied Intuition is looking to build and deploy scalable planning and control systems for commercial trucking to accelerate the adoption of safe, AI-driven machines.
Requirements
- 5+ years of experience in robotics or autonomous systems, with a focus on motion planning and control systems
- Experience in two or more of the following: behavior planning, path planning, motion planning, trajectory generation, control theory
- Demonstrated ability to architect large-scale, production software for real-world robotic or automotive systems
- Experience writing safety-critical, high-performance software using modern C++
- Experience in quadratic programming and nonlinear optimization to create robust and performant controllers
- Experience applying machine learning techniques to vehicle dynamics modeling
Responsibilities
- Drive the overall strategy, technical roadmap, and architecture for motion planning and controls dedicated to L4 autonomous trucks
- Guide the design and implementation of robust, safety-critical algorithms for various driving domains, including urban streets, highways, and diverse environmental conditions
- Grow and manage a team of world class engineers in the field of control systems for autonomous driving
- Evolve the architecture to achieve the final goal of an L4 end-to-end stack
- Work cross functionally with other teams, including Perception, Remote Assistance, Simulation, and Vehicle Operations, to ensure the robust and efficient integration of the motion planning and controls software stack
- Work across the different verticals in the company, and help create technology used across trucks and cars, for all levels of automation
Other
- We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week.
- Proven leadership experience managing engineering teams, setting goals, and overseeing technical projects
- Strong communication and problem-solving skills
- Direct experience working in the Trucking autonomy domain
- Hands-on experience developing and testing planning algorithms on an autonomous vehicle
