Udelv pioneered autonomous delivery with the first public road-enabled autonomous delivery vehicle launched in January 2018. After completing more than 1,400 automated deliveries in California in 2018, Udelv launched a second-generation vehicle at CES 2019 and has since completed more than 7,000 automated deliveries on behalf of prestigious clients in Arizona and Texas. With its patented technology that fits the needs of last- and middle-mile delivery, Udelv has garnered hundreds of millions of dollars in orders and pre-orders and is on path to become of the first AV companies to remove the safety operator on its B2B routes. Our mission is to improve people’s lives by seamlessly moving goods directly to people’s doors with a cheap, safe and sustainable technology.
As a Motion Planning Engineer, you will be responsible for the development of environment modeling, motion planning, and behavioral prediction algorithms, and software that enables an autonomous vehicle to interact with its environment. You will have the chance to work with software engineers implementing these algorithms on highly capable, next generation hardware that will allow you to explore algorithms that were traditionally too costly on edge computing systems.
- Building/Integrating software and algorithms for path planning, behavioral planning and vehicle control
- Testing algorithms in simulation, in vehicle in controlled environment, and ultimately in in vehicle in the field.
- Benchmarking and validating navigation systems for final deployment
- Developing safety engineering and fault tolerance into navigation and control systems.
- Collaborating with technical and non-technical team members to build full-stack autonomous solutions
- Performing other duties as necessary for completion of projects and achievement of goals.
- All other duties as assigned
- MS or PhD degree in CS/CE/EE/ME/Robotics or equivalent with focus on Motion/Path Planning or related field
- Publications in the field of Motion/Path Planning
- Understanding of configuration spaces and a variety of planning techniques (A*, D*, RRTs, PRMs, Convex Programming, Probabilistic Planning, etc.)
- Highly skilled in motion planning and control theory (e.g., model predictive control, vehicle dynamic modeling)
- Extensive experience with programming and algorithm design
- Experience working with large data sets
- Extensive software development experience (C/C++, Python)
- Experience with software engineering tools and linux environment (e.g., Git, CMake, CI, gdb,etc)
- Proven ability to program real-time hardware to execute computationally intensive algorithms
- Experience in Automotive Industry will be regarded. Experience in writing safety-critical code
- Familiarity with ROS and/or other message networking middleware
- SLAM / Probabilistic Filtering experience
- Reinforcement learning experience
- Experience with real-time sensor fusion (e.g. IMU, lidar, camera, odometry, radar)